Αναλυτικά logs από τα πειράματα που περιγράφονται στη μεταπτυχιακή διπλωματική εργασία.
Iteration: 1 Best Fitness: -54.7607 Best program: f1(x)=((-4.07)*x8) f2(x)=(x9-(x21/((-47.77)/(-02.29))*x1+(890.7/(-71.899))*x7+x9+(-102.04)*x8)) Iteration: 2 Best Fitness: -49.7566 Best program: f1(x)=(89.01*x8) f2(x)=(x9-(x21/((-47.77)/(-02.29))*x1+(890.7/(-71.899))*x7+x9+(-102.04)*x8)) Iteration: 3 Best Fitness: -47.5498 Best program: f1(x)=x1 f2(x)=((-432.33)*x6*cos((-7.77)*x19+867.91*x5)) Iteration: 4 Best Fitness: -41.7281 Best program: f1(x)=x27 f2(x)=((-47.733)*x6*cos((-7.7)*x28+abs(((-6.59)/88.81)*x23+x27))) Iteration: 5 Best Fitness: -37.437 Best program: f1(x)=((-4.77)*x8) f2(x)=((-47.738)*x28*cos((-7.77)*x19+867.91*x5)) ** CONFUSION MATRIX ** Number of classes: 2 86 12 15 172 PRECISION 0.8931338786 RECALL: 0.8986685583 train[0]=16.8297368647 test[0]=22.035009 class[0]=9.473684% ** CONFUSION MATRIX ** Number of classes: 2 86 12 15 172 PRECISION 0.8931338786 RECALL: 0.8986685583 train[1]=16.8297368647 test[1]=22.035009 class[1]=9.473684% ** CONFUSION MATRIX ** Number of classes: 2 86 12 15 172 PRECISION 0.8931338786 RECALL: 0.8986685583 train[2]=16.8297368647 test[2]=22.035009 class[2]=9.473684% ** CONFUSION MATRIX ** Number of classes: 2 86 12 15 172 PRECISION 0.8931338786 RECALL: 0.8986685583 train[3]=16.8297368647 test[3]=22.035009 class[3]=9.473684% ** CONFUSION MATRIX ** Number of classes: 2 86 12 15 172 PRECISION 0.8931338786 RECALL: 0.8986685583 train[4]=16.8297368647 test[4]=22.035009 class[4]=9.473684% ** CONFUSION MATRIX ** Number of classes: 2 86 12 15 172 PRECISION 0.8931338786 RECALL: 0.8986685583 train[5]=16.8297368647 test[5]=22.035009 class[5]=9.473684% ** CONFUSION MATRIX ** Number of classes: 2 86 12 15 172 PRECISION 0.8931338786 RECALL: 0.8986685583 train[6]=16.8297368647 test[6]=22.035009 class[6]=9.473684% ** CONFUSION MATRIX ** Number of classes: 2 86 12 15 172 PRECISION 0.8931338786 RECALL: 0.8986685583 train[7]=16.8297368647 test[7]=22.035009 class[7]=9.473684% ** CONFUSION MATRIX ** Number of classes: 2 86 12 15 172 PRECISION 0.8931338786 RECALL: 0.8986685583 train[8]=16.8297368647 test[8]=22.035009 class[8]=9.473684% ** CONFUSION MATRIX ** Number of classes: 2 86 12 15 172 PRECISION 0.8931338786 RECALL: 0.8986685583 train[9]=16.8297368647 test[9]=22.035009 class[9]=9.473684% ** CONFUSION MATRIX ** Number of classes: 2 86 12 15 172 PRECISION 0.8931338786 RECALL: 0.8986685583 train[10]=16.8297368647 test[10]=22.035009 class[10]=9.473684% ** CONFUSION MATRIX ** Number of classes: 2 86 12 15 172 PRECISION 0.8931338786 RECALL: 0.8986685583 train[11]=16.8297368647 test[11]=22.035009 class[11]=9.473684% ** CONFUSION MATRIX ** Number of classes: 2 86 12 15 172 PRECISION 0.8931338786 RECALL: 0.8986685583 train[12]=16.8297368647 test[12]=22.035009 class[12]=9.473684% ** CONFUSION MATRIX ** Number of classes: 2 86 12 15 172 PRECISION 0.8931338786 RECALL: 0.8986685583 train[13]=16.8297368647 test[13]=22.035009 class[13]=9.473684% ** CONFUSION MATRIX ** Number of classes: 2 86 12 15 172 PRECISION 0.8931338786 RECALL: 0.8986685583 train[14]=16.8297368647 test[14]=22.035009 class[14]=9.473684% ** CONFUSION MATRIX ** Number of classes: 2 86 12 15 172 PRECISION 0.8931338786 RECALL: 0.8986685583 train[15]=16.8297368647 test[15]=22.035009 class[15]=9.473684% ** CONFUSION MATRIX ** Number of classes: 2 86 12 15 172 PRECISION 0.8931338786 RECALL: 0.8986685583 train[16]=16.8297368647 test[16]=22.035009 class[16]=9.473684% ** CONFUSION MATRIX ** Number of classes: 2 86 12 15 172 PRECISION 0.8931338786 RECALL: 0.8986685583 train[17]=16.8297368647 test[17]=22.035009 class[17]=9.473684% ** CONFUSION MATRIX ** Number of classes: 2 86 12 15 172 PRECISION 0.8931338786 RECALL: 0.8986685583 train[18]=16.8297368647 test[18]=22.035009 class[18]=9.473684% ** CONFUSION MATRIX ** Number of classes: 2 86 12 15 172 PRECISION 0.8931338786 RECALL: 0.8986685583 train[19]=16.8297368647 test[19]=22.035009 class[19]=9.473684% ** CONFUSION MATRIX ** Number of classes: 2 86 12 15 172 PRECISION 0.8931338786 RECALL: 0.8986685583 train[20]=16.8297368647 test[20]=22.035009 class[20]=9.473684% ** CONFUSION MATRIX ** Number of classes: 2 86 12 15 172 PRECISION 0.8931338786 RECALL: 0.8986685583 train[21]=16.8297368647 test[21]=22.035009 class[21]=9.473684% ** CONFUSION MATRIX ** Number of classes: 2 86 12 15 172 PRECISION 0.8931338786 RECALL: 0.8986685583 train[22]=16.8297368647 test[22]=22.035009 class[22]=9.473684% ** CONFUSION MATRIX ** Number of classes: 2 86 12 15 172 PRECISION 0.8931338786 RECALL: 0.8986685583 train[23]=16.8297368647 test[23]=22.035009 class[23]=9.473684% ** CONFUSION MATRIX ** Number of classes: 2 86 12 15 172 PRECISION 0.8931338786 RECALL: 0.8986685583 train[24]=16.8297368647 test[24]=22.035009 class[24]=9.473684% ** CONFUSION MATRIX ** Number of classes: 2 86 12 15 172 PRECISION 0.8931338786 RECALL: 0.8986685583 train[25]=16.8297368647 test[25]=22.035009 class[25]=9.473684% ** CONFUSION MATRIX ** Number of classes: 2 86 12 15 172 PRECISION 0.8931338786 RECALL: 0.8986685583 train[26]=16.8297368647 test[26]=22.035009 class[26]=9.473684% ** CONFUSION MATRIX ** Number of classes: 2 86 12 15 172 PRECISION 0.8931338786 RECALL: 0.8986685583 train[27]=16.8297368647 test[27]=22.035009 class[27]=9.473684% ** CONFUSION MATRIX ** Number of classes: 2 86 12 15 172 PRECISION 0.8931338786 RECALL: 0.8986685583 train[28]=16.8297368647 test[28]=22.035009 class[28]=9.473684% ** CONFUSION MATRIX ** Number of classes: 2 86 12 15 172 PRECISION 0.8931338786 RECALL: 0.8986685583 train[29]=16.8297368647 test[29]=22.035009 class[29]=9.473684% ================================================= AVERAGES (TRAIN,TEST,CLASS): 16.829737 22.035009 9.4736842% AVERAGES (PRECISION,RECALL): 0.89313388 0.89866856
Iteration: 1 Best Fitness: -0.00204664 Best program: f1(x)=(x21*(3.01/(-21.4))*x14) f2(x)=((-00.73)*x34) Iteration: 2 Best Fitness: -0.00204661 Best program: f1(x)=cos((x11/94.01*x28)) f2(x)=(x31*((-96.77)*x30)) Iteration: 3 Best Fitness: -0.00204661 Best program: f1(x)=cos((x11/94.01*x28)) f2(x)=(x31*((-96.77)*x30)) Iteration: 4 Best Fitness: -0.0020466 Best program: f1(x)=cos((x11/94.91*x28)) f2(x)=(x31*((-96.77)*x27)) Iteration: 5 Best Fitness: -0.0020466 Best program: f1(x)=cos((x11/94.01*x28)) f2(x)=(x31*((-96.97)*x27)) ** CONFUSION MATRIX ** Number of classes: 26 1416 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 PRECISION -1.#IND000000 RECALL: -1.#IND000000 train[0]=0.0020466045 test[0]=0.000240 class[0]=91.384181% ... ** CONFUSION MATRIX ** Number of classes: 26 1416 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 PRECISION -1.#IND000000 RECALL: -1.#IND000000 train[29]=0.0020466045 test[29]=0.000240 class[29]=91.384181% ================================================= AVERAGES (TRAIN,TEST,CLASS): 0.0020466045 0.00023989394 91.384181% AVERAGES (PRECISION,RECALL): 0 0
# Settings Tab: Advanced # Run Date: 03/09/2025 13:55:31 # Run Time: 00:00:03 (3680 ms) @ Parameters: ============================================================ 1. Dataset File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/datasets/MLDATASETS-master/MLDATASETS-master/CLASSIFICATION/wdbc.data 2. Dataset Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.train 3. Dataset Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.test 4. Export Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.train 5. Export Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.test 6. Export Cpp File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.cpp 7. Features: 1 8. Feature Create Model: Copy 9. Feature Evaluate Model: RBF 10. Test Iters: 30 11. Random Seed: 1 12. Threads: 1 13. RBF Weights: 10 @ Results: =============================================================== ** CONFUSION MATRIX ** Number of classes: 2 17 2 4 34 PRECISION 0.8769841270 RECALL: 0.8947368421 train[0]=25.9227927775 test[0]=3.664261 class[0]=10.526316% ** CONFUSION MATRIX ** Number of classes: 2 17 2 4 34 PRECISION 0.8769841270 RECALL: 0.8947368421 train[1]=25.8800910378 test[1]=3.667537 class[1]=10.526316% ** CONFUSION MATRIX ** Number of classes: 2 17 2 4 34 PRECISION 0.8769841270 RECALL: 0.8947368421 train[2]=27.5945805897 test[2]=3.927761 class[2]=10.526316% ** CONFUSION MATRIX ** Number of classes: 2 17 2 4 34 PRECISION 0.8769841270 RECALL: 0.8947368421 train[3]=26.8536077687 test[3]=3.853917 class[3]=10.526316% ** CONFUSION MATRIX ** Number of classes: 2 17 2 4 34 PRECISION 0.8769841270 RECALL: 0.8947368421 train[4]=26.8822465012 test[4]=3.804635 class[4]=10.526316% ** CONFUSION MATRIX ** Number of classes: 2 17 2 4 34 PRECISION 0.8769841270 RECALL: 0.8947368421 train[5]=26.8536077687 test[5]=3.853917 class[5]=10.526316% ** CONFUSION MATRIX ** Number of classes: 2 17 2 4 34 PRECISION 0.8769841270 RECALL: 0.8947368421 train[6]=26.8536077687 test[6]=3.853917 class[6]=10.526316% ** CONFUSION MATRIX ** Number of classes: 2 17 2 4 34 PRECISION 0.8769841270 RECALL: 0.8947368421 train[7]=26.8536077687 test[7]=3.853917 class[7]=10.526316% ** CONFUSION MATRIX ** Number of classes: 2 17 2 4 34 PRECISION 0.8769841270 RECALL: 0.8947368421 train[8]=26.3292217687 test[8]=3.708895 class[8]=10.526316% ** CONFUSION MATRIX ** Number of classes: 2 17 2 4 34 PRECISION 0.8769841270 RECALL: 0.8947368421 train[9]=26.5818609312 test[9]=3.853080 class[9]=10.526316% ** CONFUSION MATRIX ** Number of classes: 2 17 2 4 34 PRECISION 0.8769841270 RECALL: 0.8947368421 train[10]=26.0080155506 test[10]=3.700908 class[10]=10.526316% ** CONFUSION MATRIX ** Number of classes: 2 17 2 4 34 PRECISION 0.8769841270 RECALL: 0.8947368421 train[11]=27.4946978178 test[11]=3.939192 class[11]=10.526316% ** CONFUSION MATRIX ** Number of classes: 2 17 2 4 34 PRECISION 0.8769841270 RECALL: 0.8947368421 train[12]=25.8800910378 test[12]=3.667537 class[12]=10.526316% ** CONFUSION MATRIX ** Number of classes: 2 17 2 4 34 PRECISION 0.8769841270 RECALL: 0.8947368421 train[13]=26.7564229510 test[13]=3.857585 class[13]=10.526316% ** CONFUSION MATRIX ** Number of classes: 2 17 2 4 34 PRECISION 0.8769841270 RECALL: 0.8947368421 train[14]=26.8536077687 test[14]=3.853917 class[14]=10.526316% ** CONFUSION MATRIX ** Number of classes: 2 17 2 4 34 PRECISION 0.8769841270 RECALL: 0.8947368421 train[15]=25.8800910378 test[15]=3.667537 class[15]=10.526316% ** CONFUSION MATRIX ** Number of classes: 2 17 2 4 34 PRECISION 0.8769841270 RECALL: 0.8947368421 train[16]=25.9389937738 test[16]=3.672113 class[16]=10.526316% ** CONFUSION MATRIX ** Number of classes: 2 17 2 4 34 PRECISION 0.8769841270 RECALL: 0.8947368421 train[17]=26.8717769750 test[17]=3.798392 class[17]=10.526316% ** CONFUSION MATRIX ** Number of classes: 2 17 2 4 34 PRECISION 0.8769841270 RECALL: 0.8947368421 train[18]=26.7359874057 test[18]=3.840590 class[18]=10.526316% ** CONFUSION MATRIX ** Number of classes: 2 17 2 4 34 PRECISION 0.8769841270 RECALL: 0.8947368421 train[19]=25.8800910378 test[19]=3.667537 class[19]=10.526316% ** CONFUSION MATRIX ** Number of classes: 2 17 2 4 34 PRECISION 0.8769841270 RECALL: 0.8947368421 train[20]=26.3263366638 test[20]=3.729642 class[20]=10.526316% ** CONFUSION MATRIX ** Number of classes: 2 17 2 4 34 PRECISION 0.8769841270 RECALL: 0.8947368421 train[21]=26.8536077687 test[21]=3.853917 class[21]=10.526316% ** CONFUSION MATRIX ** Number of classes: 2 17 2 4 34 PRECISION 0.8769841270 RECALL: 0.8947368421 train[22]=26.1320385424 test[22]=3.765706 class[22]=10.526316% ** CONFUSION MATRIX ** Number of classes: 2 17 2 4 34 PRECISION 0.8769841270 RECALL: 0.8947368421 train[23]=25.9389937738 test[23]=3.672113 class[23]=10.526316% ** CONFUSION MATRIX ** Number of classes: 2 18 2 3 34 PRECISION 0.9007936508 RECALL: 0.9094594595 train[24]=27.1064490730 test[24]=3.827796 class[24]=8.771930% ** CONFUSION MATRIX ** Number of classes: 2 17 2 4 34 PRECISION 0.8769841270 RECALL: 0.8947368421 train[25]=25.8800910378 test[25]=3.667537 class[25]=10.526316% ** CONFUSION MATRIX ** Number of classes: 2 17 2 4 34 PRECISION 0.8769841270 RECALL: 0.8947368421 train[26]=26.5923330196 test[26]=3.809561 class[26]=10.526316% ** CONFUSION MATRIX ** Number of classes: 2 17 2 4 34 PRECISION 0.8769841270 RECALL: 0.8947368421 train[27]=25.9389937738 test[27]=3.672114 class[27]=10.526316% ** CONFUSION MATRIX ** Number of classes: 2 17 2 4 34 PRECISION 0.8769841270 RECALL: 0.8947368421 train[28]=26.1320385424 test[28]=3.765706 class[28]=10.526316% ** CONFUSION MATRIX ** Number of classes: 2 17 3 4 33 PRECISION 0.8630952381 RECALL: 0.8709459459 train[29]=31.5131309240 test[29]=4.978596 class[29]=12.280702% ============================================================================= AVERAGES (TRAIN,TEST,CLASS): 26.643967 3.8149945 10.526316% AVERAGES (PRECISION,RECALL): 0.87731481 0.89443457
# Settings Tab: Advanced # Run Date: 03/09/2025 14:02:18 # Run Time: 00:53:54 (3234164 ms) @ Parameters: ============================================================ 1. Dataset File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/datasets/MLDATASETS-master/QFC_GUI_datasets/Classification/wdbc.data 2. Dataset Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.train 3. Dataset Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.test 4. Export Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.train 5. Export Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.test 6. Export Cpp File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.cpp 7. Features: 1 8. Feature Create Model: Copy 9. Feature Evaluate Model: Neural 10. Test Iters: 30 11. Random Seed: 1 12. Threads: 1 13. Neural Weights: 10 14. Neural Training Method: Genetic 15. BFGS Iteration: 2001 16. GN Chromosomes: 500 17. GN Max Generations: 200 18. GN Selection Rate: 0,10 19. GN Mutation Rate: 0,05 20. GN Local Search Rate: 0,00 21. GN Local Search Method: BFGS @ Results: =============================================================== ** CONFUSION MATRIX ** Number of classes: 2 19 1 2 35 PRECISION 0.9384920635 RECALL: 0.9479729730 train[0]=12.6381656772 test[0]=2.314435 class[0]=5.263158% ** CONFUSION MATRIX ** Number of classes: 2 19 1 2 35 PRECISION 0.9384920635 RECALL: 0.9479729730 train[1]=12.6381656772 test[1]=2.314435 class[1]=5.263158% ** CONFUSION MATRIX ** Number of classes: 2 19 1 2 35 PRECISION 0.9384920635 RECALL: 0.9479729730 train[2]=12.6381656772 test[2]=2.314435 class[2]=5.263158% ** CONFUSION MATRIX ** Number of classes: 2 19 1 2 35 PRECISION 0.9384920635 RECALL: 0.9479729730 train[3]=12.6381656772 test[3]=2.314435 class[3]=5.263158% ** CONFUSION MATRIX ** Number of classes: 2 19 1 2 35 PRECISION 0.9384920635 RECALL: 0.9479729730 train[4]=12.6381656772 test[4]=2.314435 class[4]=5.263158% ** CONFUSION MATRIX ** Number of classes: 2 19 1 2 35 PRECISION 0.9384920635 RECALL: 0.9479729730 train[5]=12.6381656772 test[5]=2.314435 class[5]=5.263158% ** CONFUSION MATRIX ** Number of classes: 2 19 1 2 35 PRECISION 0.9384920635 RECALL: 0.9479729730 train[6]=12.6381656772 test[6]=2.314435 class[6]=5.263158% ** CONFUSION MATRIX ** Number of classes: 2 19 1 2 35 PRECISION 0.9384920635 RECALL: 0.9479729730 train[7]=12.6381656772 test[7]=2.314435 class[7]=5.263158% ** CONFUSION MATRIX ** Number of classes: 2 19 1 2 35 PRECISION 0.9384920635 RECALL: 0.9479729730 train[8]=12.6381656772 test[8]=2.314435 class[8]=5.263158% ** CONFUSION MATRIX ** Number of classes: 2 19 1 2 35 PRECISION 0.9384920635 RECALL: 0.9479729730 train[9]=12.6381656772 test[9]=2.314435 class[9]=5.263158% ** CONFUSION MATRIX ** Number of classes: 2 19 1 2 35 PRECISION 0.9384920635 RECALL: 0.9479729730 train[10]=12.6381656772 test[10]=2.314435 class[10]=5.263158% ** CONFUSION MATRIX ** Number of classes: 2 19 1 2 35 PRECISION 0.9384920635 RECALL: 0.9479729730 train[11]=12.6381656772 test[11]=2.314435 class[11]=5.263158% ** CONFUSION MATRIX ** Number of classes: 2 19 1 2 35 PRECISION 0.9384920635 RECALL: 0.9479729730 train[12]=12.6381656772 test[12]=2.314435 class[12]=5.263158% ** CONFUSION MATRIX ** Number of classes: 2 19 1 2 35 PRECISION 0.9384920635 RECALL: 0.9479729730 train[13]=12.6381656772 test[13]=2.314435 class[13]=5.263158% ** CONFUSION MATRIX ** Number of classes: 2 19 1 2 35 PRECISION 0.9384920635 RECALL: 0.9479729730 train[14]=12.6381656772 test[14]=2.314435 class[14]=5.263158% ** CONFUSION MATRIX ** Number of classes: 2 19 1 2 35 PRECISION 0.9384920635 RECALL: 0.9479729730 train[15]=12.6381656772 test[15]=2.314435 class[15]=5.263158% ** CONFUSION MATRIX ** Number of classes: 2 19 1 2 35 PRECISION 0.9384920635 RECALL: 0.9479729730 train[16]=12.6381656772 test[16]=2.314435 class[16]=5.263158% ** CONFUSION MATRIX ** Number of classes: 2 19 1 2 35 PRECISION 0.9384920635 RECALL: 0.9479729730 train[17]=12.6381656772 test[17]=2.314435 class[17]=5.263158% ** CONFUSION MATRIX ** Number of classes: 2 19 1 2 35 PRECISION 0.9384920635 RECALL: 0.9479729730 train[18]=12.6381656772 test[18]=2.314435 class[18]=5.263158% ** CONFUSION MATRIX ** Number of classes: 2 19 1 2 35 PRECISION 0.9384920635 RECALL: 0.9479729730 train[19]=12.6381656772 test[19]=2.314435 class[19]=5.263158% ** CONFUSION MATRIX ** Number of classes: 2 19 1 2 35 PRECISION 0.9384920635 RECALL: 0.9479729730 train[20]=12.6381656772 test[20]=2.314435 class[20]=5.263158% ** CONFUSION MATRIX ** Number of classes: 2 19 1 2 35 PRECISION 0.9384920635 RECALL: 0.9479729730 train[21]=12.6381656772 test[21]=2.314435 class[21]=5.263158% ** CONFUSION MATRIX ** Number of classes: 2 19 1 2 35 PRECISION 0.9384920635 RECALL: 0.9479729730 train[22]=12.6381656772 test[22]=2.314435 class[22]=5.263158% ** CONFUSION MATRIX ** Number of classes: 2 19 1 2 35 PRECISION 0.9384920635 RECALL: 0.9479729730 train[23]=12.6381656772 test[23]=2.314435 class[23]=5.263158% ** CONFUSION MATRIX ** Number of classes: 2 19 1 2 35 PRECISION 0.9384920635 RECALL: 0.9479729730 train[24]=12.6381656772 test[24]=2.314435 class[24]=5.263158% ** CONFUSION MATRIX ** Number of classes: 2 19 1 2 35 PRECISION 0.9384920635 RECALL: 0.9479729730 train[25]=12.6381656772 test[25]=2.314435 class[25]=5.263158% ** CONFUSION MATRIX ** Number of classes: 2 19 1 2 35 PRECISION 0.9384920635 RECALL: 0.9479729730 train[26]=12.6381656772 test[26]=2.314435 class[26]=5.263158% ** CONFUSION MATRIX ** Number of classes: 2 19 1 2 35 PRECISION 0.9384920635 RECALL: 0.9479729730 train[27]=12.6381656772 test[27]=2.314435 class[27]=5.263158% ** CONFUSION MATRIX ** Number of classes: 2 19 1 2 35 PRECISION 0.9384920635 RECALL: 0.9479729730 train[28]=12.6381656772 test[28]=2.314435 class[28]=5.263158% ** CONFUSION MATRIX ** Number of classes: 2 19 1 2 35 PRECISION 0.9384920635 RECALL: 0.9479729730 train[29]=12.6381656772 test[29]=2.314435 class[29]=5.263158% ============================================================================= AVERAGES (TRAIN,TEST,CLASS): 12.638166 2.3144347 5.2631579% AVERAGES (PRECISION,RECALL): 0.93849206 0.94797297
# Settings Tab: Advanced # Run Date: 03/09/2025 16:15:23 # Run Time: 00:54:50 (3290500 ms) @ Parameters: ============================================================ 1. Dataset File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/datasets/MLDATASETS-master/QFC_GUI_datasets/Classification/wdbc.data 2. Dataset Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.train 3. Dataset Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.test 4. Export Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.train 5. Export Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.test 6. Export Cpp File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.cpp 7. Features: 2 8. Feature Create Model: RBF 9. Feature Evaluate Model: RBF 10. Test Iters: 30 11. Random Seed: 1 12. Threads: 1 13. RBF Weights: 10 14. GE Chromosomes: 500 15. GE Max Generations: 200 16. GE Selection Rate: 0,10 17. GE Mutation Rate: 0,05 18. GE Length: 40 19. GE Local Search Generations: 20 20. GE Local Search Rate: 0,05 21. GE Local Search Method: Crossover 22. GE Method: Genetic @ Results: =============================================================== Iteration: 1 Best Fitness: -19.0007 Best program: f1(x)=(9.4/(-65.95))*x3 f2(x)=(x15-((-4.79)/(-3.321))*x29+51.5*x25+abs(x10+x21)) Iteration: 2 Best Fitness: -17.165 Best program: f1(x)=(46.4*x8) f2(x)=(x15-((-4.79)/(-3.321))*x29+51.5*x25+abs(x10+x21)) Iteration: 3 Best Fitness: -16.5999 Best program: f1(x)=abs(log((265.95/2.4)*x27+((-170.366)/(-71.538))*x16+((-8.9)/(-3.65))*x8+x16)) f2(x)=(x15-((-4.79)/(-3.391))*x29+51.3*x25+abs(x10+x21)) Iteration: 4 Best Fitness: -15.9128 Best program: f1(x)=abs(log((265.95/2.4)*x27+((-170.366)/(-71.538))*x16+((-8.9)/(-3.65))*x8+x16)) f2(x)=(x15-((-4.79)/(-4.891))*x29+51.9*x25+abs(x10+x21)) Iteration: 5 Best Fitness: -15.7504 Best program: f1(x)=abs(log((265.95/2.4)*x27+((-170.366)/(-71.538))*x16+((-8.9)/(-3.65))*x8+x16)) f2(x)=(x15-((-4.79)/(-4.891))*x29+59.5*x25+abs(x10+x21)) Iteration: 6 Best Fitness: -14.8172 Best program: f1(x)=abs(log((265.95/2.4)*x27+((-170.366)/(-71.538))*x16+((-8.9)/3.65)*x8+x16)) f2(x)=(x15-((-4.73)/1.321)*x29+51.5*x25+abs(x10+x21)) Iteration: 7 Best Fitness: -14.6054 Best program: f1(x)=(665.7/(-61.582))*x7 f2(x)=(x15-((-4.70)/1.321)*x29+51.5*x25+abs(x10+x21)) Iteration: 8 Best Fitness: -14.5642 Best program: f1(x)=(665.7/(-61.582))*x7 f2(x)=(x15-((-4.73)/1.321)*x29+51.5*x25+abs(x10+x21)) Iteration: 9 Best Fitness: -14.5583 Best program: f1(x)=(665.7/(-61.562))*x7 f2(x)=(x15-((-4.73)/1.321)*x29+51.5*x25+abs(x30+x21)) Iteration: 10 Best Fitness: -14.326 Best program: f1(x)=abs(log((265.96/244.2)*x2+x22+x11)) f2(x)=(x13-((-47.9)/5.891)*x29+51.5*x25+abs(x10+x21)) Iteration: 11 Best Fitness: -12.8215 Best program: f1(x)=abs(log((265.96/244.2)*x2+x22+x11)) f2(x)=(x13-((-47.9)/4.891)*x29+51.5*x25+abs(x10+x21)) Iteration: 12 Best Fitness: -12.8215 Best program: f1(x)=abs(log((265.96/244.2)*x2+x22+x11)) f2(x)=(x13-((-47.9)/4.891)*x29+51.5*x25+abs(x10+x21)) Iteration: 13 Best Fitness: -12.5198 Best program: f1(x)=abs(log((235.95/274.215)*x22+x6)) f2(x)=(x13-((-47.9)/4.891)*x29+51.5*x25+abs(x10+x21)) Iteration: 14 Best Fitness: -12.2842 Best program: f1(x)=abs(sqrt((0.776*x22))) f2(x)=(x13-((-47.9)/4.896)*x29+51.5*x25+abs(x10+x21)) Iteration: 15 Best Fitness: -12.202 Best program: f1(x)=abs(sqrt((0.776*x22))) f2(x)=(x13-((-47.9)/4.891)*x29+61.5*x25+abs(x29+x21)) Iteration: 16 Best Fitness: -12.202 Best program: f1(x)=abs(sqrt((0.776*x22))) f2(x)=(x13-((-47.9)/4.891)*x29+61.5*x25+abs(x29+x21)) Iteration: 17 Best Fitness: -12.202 Best program: f1(x)=abs(sqrt((0.776*x22))) f2(x)=(x13-((-47.9)/4.891)*x29+61.5*x25+abs(x29+x21)) Iteration: 18 Best Fitness: -12.202 Best program: f1(x)=abs(sqrt((0.776*x22))) f2(x)=(x13-((-47.9)/4.891)*x29+61.5*x25+abs(x29+x21)) Iteration: 19 Best Fitness: -12.1969 Best program: f1(x)=abs(sqrt((0.776*x22))) f2(x)=(x13-((-47.3)/4.896)*x29+56.5*x25+abs(x8+x21)) Iteration: 20 Best Fitness: -11.7345 Best program: f1(x)=abs(sqrt((2.718*x22))) f2(x)=(x13-((-47.9)/4.891)*x29+61.5*x25+abs(x29+x21)) Iteration: 21 Best Fitness: -11.7345 Best program: f1(x)=abs(sqrt((2.718*x22))) f2(x)=(x13-((-47.9)/4.891)*x29+61.5*x25+abs(x29+x21)) Iteration: 22 Best Fitness: -11.7345 Best program: f1(x)=abs(sqrt((2.718*x22))) f2(x)=(x13-((-47.9)/4.891)*x29+61.5*x25+abs(x29+x21)) Iteration: 23 Best Fitness: -11.7298 Best program: f1(x)=abs(sqrt((2.718*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 24 Best Fitness: -11.7298 Best program: f1(x)=abs(sqrt((2.718*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 25 Best Fitness: -11.7298 Best program: f1(x)=abs(sqrt((2.718*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 26 Best Fitness: -11.7275 Best program: f1(x)=abs(sqrt((2.711*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 27 Best Fitness: -11.7275 Best program: f1(x)=abs(sqrt((2.711*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 28 Best Fitness: -11.7275 Best program: f1(x)=abs(sqrt((2.711*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 29 Best Fitness: -11.7275 Best program: f1(x)=abs(sqrt((2.711*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 30 Best Fitness: -11.7275 Best program: f1(x)=abs(sqrt((2.711*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 31 Best Fitness: -11.7275 Best program: f1(x)=abs(sqrt((2.711*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 32 Best Fitness: -11.7275 Best program: f1(x)=abs(sqrt((2.711*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 33 Best Fitness: -11.7275 Best program: f1(x)=abs(sqrt((2.711*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 34 Best Fitness: -11.7275 Best program: f1(x)=abs(sqrt((2.711*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 35 Best Fitness: -11.7275 Best program: f1(x)=abs(sqrt((2.711*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 36 Best Fitness: -11.7275 Best program: f1(x)=abs(sqrt((2.711*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 37 Best Fitness: -11.7275 Best program: f1(x)=abs(sqrt((2.711*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 38 Best Fitness: -11.7275 Best program: f1(x)=abs(sqrt((2.711*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 39 Best Fitness: -11.7275 Best program: f1(x)=abs(sqrt((2.711*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 40 Best Fitness: -11.7269 Best program: f1(x)=abs(sqrt((2.711*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.7*x25+abs(x29+x21)) Iteration: 41 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 42 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 43 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 44 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 45 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 46 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 47 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 48 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 49 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 50 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 51 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 52 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 53 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 54 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 55 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 56 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 57 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 58 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 59 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 60 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 61 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 62 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 63 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 64 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 65 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 66 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 67 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 68 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 69 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 70 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 71 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 72 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 73 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 74 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 75 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 76 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 77 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 78 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 79 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 80 Best Fitness: -11.7214 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 81 Best Fitness: -11.721 Best program: f1(x)=abs(sqrt((2.700*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21)) Iteration: 82 Best Fitness: -11.5701 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21+((-9.72)/(-9.87))*x29+((-9.54)/(-318.246))*x22)) Iteration: 83 Best Fitness: -11.5701 Best program: f1(x)=abs(sqrt((2.701*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21+((-9.72)/(-9.87))*x29+((-9.54)/(-318.246))*x22)) Iteration: 84 Best Fitness: -11.5697 Best program: f1(x)=abs(sqrt((2.700*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21+((-9.72)/(-9.87))*x29+((-9.54)/(-318.246))*x22)) Iteration: 85 Best Fitness: -11.5694 Best program: f1(x)=abs(sqrt((2.700*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21+((-9.72)/(-9.79))*x29+((-9.54)/(-318.046))*x22)) Iteration: 86 Best Fitness: -11.5694 Best program: f1(x)=abs(sqrt((2.700*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21+((-9.72)/(-9.79))*x29+((-9.54)/(-318.046))*x22)) Iteration: 87 Best Fitness: -11.5694 Best program: f1(x)=abs(sqrt((2.700*x22))) f2(x)=(x13-((-47.9)/4.831)*x29+61.6*x25+abs(x29+x21+((-9.73)/(-9.79))*x29+((-9.54)/(-318.046))*x22)) Iteration: 88 Best Fitness: -11.464 Best program: f1(x)=abs(sqrt((2.704*x22))) f2(x)=(x13-((-47.9)/3.831)*x29+61.6*x25+abs(x29+x21+((-7.72)/(-9.87))*x29+((-9.53)/(-318.246))*x22)) Iteration: 89 Best Fitness: -11.4367 Best program: f1(x)=abs(sqrt((2.700*x22))) f2(x)=(x13-((-47.9)/3.831)*x29+61.6*x25+abs(x29+x21+((-7.72)/(-9.87))*x29+((-9.53)/(-318.246))*x22)) Iteration: 90 Best Fitness: -11.4335 Best program: f1(x)=abs(sqrt((2.300*x22))) f2(x)=(x13-((-47.9)/3.831)*x29+61.6*x25+abs(x29+x21+((-7.72)/(-9.87))*x29+((-9.53)/(-318.246))*x22)) Iteration: 91 Best Fitness: -11.4029 Best program: f1(x)=abs(sqrt((2.700*x22))) f2(x)=(x13-((-47.9)/3.831)*x29+51.6*x25+abs(x29+x21+((-7.72)/(-9.87))*x29+((-9.53)/(-318.245))*x22)) Iteration: 92 Best Fitness: -11.3084 Best program: f1(x)=abs(sqrt((2.309*x22))) f2(x)=(x13-((-47.9)/3.808)*x29+61.6*x25+abs(x29+x21+((-7.72)/(-9.87))*x29+((-9.53)/(-088.246))*x22)) Iteration: 93 Best Fitness: -11.0606 Best program: f1(x)=abs(sqrt((2.309*x22))) f2(x)=(x13-((-47.9)/4.808)*x29+61.6*x25+abs(x29+x21+((-9.72)/(-9.87))*x29+((-9.54)/(-088.246))*x22)) Iteration: 94 Best Fitness: -10.716 Best program: f1(x)=abs(sqrt((27.001*x8))) f2(x)=(x13-((-47.9)/3.806)*x29+61.6*x25+abs(x29+x21+((-7.72)/(-9.87))*x29+((-9.53)/(-068.246))*x22)) Iteration: 95 Best Fitness: -10.2588 Best program: f1(x)=log(sqrt((527.119*x10))) f2(x)=(x13-((-47.9)/3.806)*x29+61.6*x25+abs(x29+x21+((-7.72)/(-9.87))*x29+((-9.53)/(-068.246))*x22)) Iteration: 96 Best Fitness: -9.61305 Best program: f1(x)=((-625.7)/(-911.227))*x10 f2(x)=(x13-((-47.9)/3.804)*x29+61.6*x25+abs(x29+x21+(7.55/(-9.87))*x29+((-9.53)/(-048.246))*x22)) Iteration: 97 Best Fitness: -9.49524 Best program: f1(x)=abs(sqrt((27.001*x8))) f2(x)=(x13-((-47.9)/3.804)*x29+51.2*x25+abs(x29+x21+((-7.53)/(-9.87))*x29+((-9.53)/(-048.245))*x22)) Iteration: 98 Best Fitness: -9.49524 Best program: f1(x)=abs(sqrt((27.001*x8))) f2(x)=(x13-((-47.9)/3.804)*x29+51.2*x25+abs(x29+x21+((-7.53)/(-9.87))*x29+((-9.53)/(-048.245))*x22)) Iteration: 99 Best Fitness: -9.49524 Best program: f1(x)=abs(sqrt((27.001*x8))) f2(x)=(x13-((-47.9)/3.804)*x29+51.2*x25+abs(x29+x21+((-7.53)/(-9.87))*x29+((-9.53)/(-048.245))*x22)) Iteration: 100 Best Fitness: -9.49524 Best program: f1(x)=abs(sqrt((27.001*x8))) f2(x)=(x13-((-47.9)/3.804)*x29+51.2*x25+abs(x29+x21+((-7.53)/(-9.87))*x29+((-9.53)/(-048.245))*x22)) Iteration: 101 Best Fitness: -9.4952 Best program: f1(x)=abs(sqrt((27.001*x8))) f2(x)=(x13-((-47.9)/3.804)*x29+51.2*x25+abs(x29+x21+((-7.52)/(-9.87))*x29+((-9.53)/(-048.245))*x22)) Iteration: 102 Best Fitness: -9.46126 Best program: f1(x)=abs(sqrt((52.001*x8))) f2(x)=(x13-((-47.9)/3.804)*x29+51.2*x25+abs(x29+x21+((-7.55)/(-9.87))*x29+((-9.53)/(-048.245))*x22)) Iteration: 103 Best Fitness: -9.45925 Best program: f1(x)=abs(sqrt((27.001*x8))) f2(x)=(x13-((-47.9)/3.804)*x29+51.4*x25+abs(x29+x21+((-7.65)/(-9.87))*x29+((-9.53)/(-048.245))*x22)) Iteration: 104 Best Fitness: -9.45925 Best program: f1(x)=abs(sqrt((27.001*x8))) f2(x)=(x13-((-47.9)/3.804)*x29+51.4*x25+abs(x29+x21+((-7.65)/(-9.87))*x29+((-9.53)/(-048.245))*x22)) Iteration: 105 Best Fitness: -9.30867 Best program: f1(x)=abs(sqrt((27.001*x8))) f2(x)=(x13-((-47.9)/3.804)*x29+51.2*x25+abs(x27+x21+((-7.52)/(-9.87))*x29+((-9.53)/(-048.245))*x22)) Iteration: 106 Best Fitness: -9.30867 Best program: f1(x)=abs(sqrt((27.001*x8))) f2(x)=(x13-((-47.9)/3.804)*x29+51.2*x25+abs(x27+x21+((-7.52)/(-9.87))*x29+((-9.53)/(-048.245))*x22)) Iteration: 107 Best Fitness: -9.24637 Best program: f1(x)=abs(log((573.002*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.2*x25+abs(x27+x21+((-7.52)/(-9.87))*x29+((-9.53)/(-048.245))*x22)) Iteration: 108 Best Fitness: -9.24637 Best program: f1(x)=abs(log((573.002*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.2*x25+abs(x27+x21+((-7.52)/(-9.87))*x29+((-9.53)/(-048.245))*x22)) Iteration: 109 Best Fitness: -8.91461 Best program: f1(x)=abs(sin((523.017*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-7.52)/(-9.87))*x29+((-9.53)/(-048.245))*x22)) Iteration: 110 Best Fitness: -8.91461 Best program: f1(x)=abs(sin((523.017*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-7.52)/(-9.87))*x29+((-9.53)/(-048.245))*x22)) Iteration: 111 Best Fitness: -8.91461 Best program: f1(x)=abs(sin((523.017*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-7.52)/(-9.87))*x29+((-9.53)/(-048.245))*x22)) Iteration: 112 Best Fitness: -8.91461 Best program: f1(x)=abs(sin((523.017*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-7.52)/(-9.87))*x29+((-9.53)/(-048.245))*x22)) Iteration: 113 Best Fitness: -8.91455 Best program: f1(x)=abs(sin((523.019*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-7.52)/(-9.89))*x29+((-9.53)/(-048.245))*x22)) Iteration: 114 Best Fitness: -8.91247 Best program: f1(x)=abs(sin((523.017*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-5.52)/(-9.87))*x29+((-9.53)/(-048.245))*x22)) Iteration: 115 Best Fitness: -8.91174 Best program: f1(x)=abs(sin((523.017*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-3.52)/(-9.87))*x29+((-9.53)/(-048.245))*x22)) Iteration: 116 Best Fitness: -8.91055 Best program: f1(x)=abs(sin(((-523.119)*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-5.52)/(-9.87))*x29+((-9.53)/(-048.245))*x22)) Iteration: 117 Best Fitness: -8.90959 Best program: f1(x)=abs(sin((523.119*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-3.52)/(-9.87))*x29+((-9.53)/(-048.245))*x22)) Iteration: 118 Best Fitness: -8.90613 Best program: f1(x)=abs(sin((523.816*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-1.52)/(-9.87))*x29+((-9.53)/(-048.245))*x22)) Iteration: 119 Best Fitness: -8.90461 Best program: f1(x)=abs(sin((523.907*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-1.52)/(-9.87))*x29+((-9.53)/(-048.245))*x22)) Iteration: 120 Best Fitness: -8.90461 Best program: f1(x)=abs(sin((523.907*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-1.52)/(-9.87))*x29+((-9.53)/(-048.245))*x22)) Iteration: 121 Best Fitness: -8.90461 Best program: f1(x)=abs(sin((523.907*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-1.52)/(-9.87))*x29+((-9.53)/(-048.245))*x22)) Iteration: 122 Best Fitness: -8.90461 Best program: f1(x)=abs(sin((523.907*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-1.52)/(-9.87))*x29+((-9.53)/(-048.245))*x22)) Iteration: 123 Best Fitness: -8.90461 Best program: f1(x)=abs(sin((523.907*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-1.52)/(-9.87))*x29+((-9.53)/(-048.245))*x22)) Iteration: 124 Best Fitness: -8.90337 Best program: f1(x)=abs(sin((523.41*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-3.52)/(-9.87))*x29+((-9.53)/(-048.045))*x22)) Iteration: 125 Best Fitness: -8.90337 Best program: f1(x)=abs(sin((523.41*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-3.52)/(-9.87))*x29+((-9.53)/(-048.045))*x22)) Iteration: 126 Best Fitness: -8.90337 Best program: f1(x)=abs(sin(((-523.407)*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-3.52)/(-9.87))*x29+((-9.53)/(-048.045))*x22)) Iteration: 127 Best Fitness: -8.90337 Best program: f1(x)=abs(sin(((-523.407)*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-3.52)/(-9.87))*x29+((-9.53)/(-048.045))*x22)) Iteration: 128 Best Fitness: -8.90337 Best program: f1(x)=abs(sin(((-523.407)*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-3.52)/(-9.87))*x29+((-9.53)/(-048.045))*x22)) Iteration: 129 Best Fitness: -8.90337 Best program: f1(x)=abs(sin(((-523.407)*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-3.52)/(-9.87))*x29+((-9.53)/(-048.045))*x22)) Iteration: 130 Best Fitness: -8.90336 Best program: f1(x)=abs(sin((523.402*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-3.52)/(-9.87))*x29+((-9.53)/(-048.045))*x22)) Iteration: 131 Best Fitness: -8.90336 Best program: f1(x)=abs(sin((523.402*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-3.52)/(-9.87))*x29+((-9.53)/(-048.045))*x22)) Iteration: 132 Best Fitness: -8.90336 Best program: f1(x)=abs(sin((523.402*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-3.52)/(-9.87))*x29+((-9.53)/(-048.045))*x22)) Iteration: 133 Best Fitness: -8.90336 Best program: f1(x)=abs(sin((523.402*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-3.52)/(-9.89))*x29+((-9.53)/(-048.045))*x22)) Iteration: 134 Best Fitness: -8.90336 Best program: f1(x)=abs(sin((523.402*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-3.52)/(-9.89))*x29+((-9.53)/(-048.045))*x22)) Iteration: 135 Best Fitness: -8.90336 Best program: f1(x)=abs(sin((523.402*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-3.52)/(-9.89))*x29+((-9.53)/(-048.045))*x22)) Iteration: 136 Best Fitness: -8.90336 Best program: f1(x)=abs(sin((523.402*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-3.52)/(-9.89))*x29+((-9.53)/(-048.045))*x22)) Iteration: 137 Best Fitness: -8.90336 Best program: f1(x)=abs(sin((523.402*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-3.52)/(-9.89))*x29+((-9.53)/(-048.045))*x22)) Iteration: 138 Best Fitness: -8.90336 Best program: f1(x)=abs(sin((523.402*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-3.52)/(-9.89))*x29+((-9.53)/(-048.045))*x22)) Iteration: 139 Best Fitness: -8.90336 Best program: f1(x)=abs(sin((523.402*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-3.52)/(-9.89))*x29+((-9.53)/(-048.045))*x22)) Iteration: 140 Best Fitness: -8.90336 Best program: f1(x)=abs(sin((523.402*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-3.52)/(-9.89))*x29+((-9.53)/(-048.045))*x22)) Iteration: 141 Best Fitness: -8.87456 Best program: f1(x)=abs(sin((523.402*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-3.52)/(-9.89))*x29+((-9.93)/(-048.045))*x22)) Iteration: 142 Best Fitness: -8.8707 Best program: f1(x)=abs(sin((523.202*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-3.52)/(-9.89))*x29+((-9.93)/(-048.045))*x22)) Iteration: 143 Best Fitness: -8.8707 Best program: f1(x)=abs(sin((523.202*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-3.52)/(-9.89))*x29+((-9.93)/(-048.045))*x22)) Iteration: 144 Best Fitness: -8.87031 Best program: f1(x)=abs(sin((523.100*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-3.52)/(-9.89))*x29+((-9.93)/(-048.045))*x22)) Iteration: 145 Best Fitness: -8.87021 Best program: f1(x)=abs(sin(((-523.102)*x10))) f2(x)=(x13-((-47.9)/3.804)*x29+51.9*x25+abs(x27+x21+((-3.22)/(-9.89))*x29+((-9.93)/(-048.045))*x22)) Iteration: 146 Best Fitness: -8.86867 Best program: f1(x)=abs(sin((523.402*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.9*x25+abs(x27+x21+((-3.51)/(-9.89))*x29+((-9.93)/(-048.045))*x22)) Iteration: 147 Best Fitness: -8.86867 Best program: f1(x)=abs(sin((523.402*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.9*x25+abs(x27+x21+((-3.51)/(-9.89))*x29+((-9.93)/(-048.045))*x22)) Iteration: 148 Best Fitness: -8.86867 Best program: f1(x)=abs(sin((523.402*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.9*x25+abs(x27+x21+((-3.51)/(-9.89))*x29+((-9.93)/(-048.045))*x22)) Iteration: 149 Best Fitness: -8.86798 Best program: f1(x)=abs(sin((523.402*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.9*x25+abs(x27+x21+((-3.42)/(-9.89))*x29+((-9.93)/(-048.045))*x22)) Iteration: 150 Best Fitness: -8.86798 Best program: f1(x)=abs(sin((523.402*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.9*x25+abs(x27+x21+((-3.42)/(-9.89))*x29+((-9.93)/(-048.045))*x22)) Iteration: 151 Best Fitness: -8.86798 Best program: f1(x)=abs(sin((523.402*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.9*x25+abs(x27+x21+((-3.42)/(-9.89))*x29+((-9.93)/(-048.045))*x22)) Iteration: 152 Best Fitness: -8.86798 Best program: f1(x)=abs(sin((523.402*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.9*x25+abs(x27+x21+((-3.42)/(-9.89))*x29+((-9.93)/(-048.045))*x22)) Iteration: 153 Best Fitness: -8.85602 Best program: f1(x)=abs(sin(((-523.106)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.9*x25+abs(x27+x21+((-1.52)/(-9.89))*x29+((-9.93)/(-048.045))*x22)) Iteration: 154 Best Fitness: -8.85602 Best program: f1(x)=abs(sin(((-523.106)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.9*x25+abs(x27+x21+((-1.52)/(-9.89))*x29+((-9.93)/(-048.045))*x22)) Iteration: 155 Best Fitness: -8.85602 Best program: f1(x)=abs(sin(((-523.106)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.9*x25+abs(x27+x21+((-1.52)/(-9.89))*x29+((-9.93)/(-048.045))*x22)) Iteration: 156 Best Fitness: -8.85602 Best program: f1(x)=abs(sin(((-523.106)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.9*x25+abs(x27+x21+((-1.52)/(-9.89))*x29+((-9.93)/(-048.045))*x22)) Iteration: 157 Best Fitness: -8.85602 Best program: f1(x)=abs(sin(((-523.106)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.9*x25+abs(x27+x21+((-1.52)/(-9.89))*x29+((-9.93)/(-048.045))*x22)) Iteration: 158 Best Fitness: -8.85602 Best program: f1(x)=abs(sin(((-523.106)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.9*x25+abs(x27+x21+((-1.52)/(-9.89))*x29+((-9.93)/(-048.045))*x22)) Iteration: 159 Best Fitness: -8.85602 Best program: f1(x)=abs(sin(((-523.106)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.9*x25+abs(x27+x21+((-1.52)/(-9.89))*x29+((-9.93)/(-048.045))*x22)) Iteration: 160 Best Fitness: -8.856 Best program: f1(x)=abs(sin((523.107*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.9*x25+abs(x27+x21+((-1.52)/(-9.89))*x29+((-9.93)/(-048.045))*x22)) Iteration: 161 Best Fitness: -8.85231 Best program: f1(x)=abs(sin(((-523.402)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.9*x25+abs(x27+x21+((-1.56)/(-9.81))*x29+((-9.93)/(-048.045))*x22)) Iteration: 162 Best Fitness: -8.84697 Best program: f1(x)=abs(sin(((-523.106)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(3.42/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 163 Best Fitness: -8.84697 Best program: f1(x)=abs(sin(((-523.106)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(3.42/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 164 Best Fitness: -8.8248 Best program: f1(x)=abs(sin(((-523.106)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.42/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 165 Best Fitness: -8.82478 Best program: f1(x)=abs(sin((523.107*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.42/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 166 Best Fitness: -8.81941 Best program: f1(x)=abs(sin(((-523.806)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.42/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 167 Best Fitness: -8.81941 Best program: f1(x)=abs(sin(((-523.806)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.42/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 168 Best Fitness: -8.81914 Best program: f1(x)=abs(sin(((-523.806)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.42/(-9.81))*x29+((-9.93)/(-048.005))*x22)) Iteration: 169 Best Fitness: -8.81896 Best program: f1(x)=abs(sin(((-523.806)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.52/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 170 Best Fitness: -8.81896 Best program: f1(x)=abs(sin(((-523.806)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.52/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 171 Best Fitness: -8.81895 Best program: f1(x)=abs(sin(((-523.802)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.52/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 172 Best Fitness: -8.81895 Best program: f1(x)=abs(sin(((-523.802)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.52/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 173 Best Fitness: -8.81895 Best program: f1(x)=abs(sin(((-523.802)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.52/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 174 Best Fitness: -8.81895 Best program: f1(x)=abs(sin(((-523.802)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.52/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 175 Best Fitness: -8.81895 Best program: f1(x)=abs(sin(((-523.802)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.52/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 176 Best Fitness: -8.81895 Best program: f1(x)=abs(sin(((-523.802)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.52/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 177 Best Fitness: -8.81895 Best program: f1(x)=abs(sin(((-523.802)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.52/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 178 Best Fitness: -8.81895 Best program: f1(x)=abs(sin(((-523.802)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.52/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 179 Best Fitness: -8.81895 Best program: f1(x)=abs(sin(((-523.802)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.52/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 180 Best Fitness: -8.81895 Best program: f1(x)=abs(sin(((-523.802)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.52/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 181 Best Fitness: -8.8189 Best program: f1(x)=abs(sin(((-523.802)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.53/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 182 Best Fitness: -8.81889 Best program: f1(x)=abs(sin(((-523.702)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.53/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 183 Best Fitness: -8.81888 Best program: f1(x)=abs(sin((523.732*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.52/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 184 Best Fitness: -8.81888 Best program: f1(x)=abs(sin((523.732*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.52/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 185 Best Fitness: -8.81884 Best program: f1(x)=abs(sin((523.732*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.53/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 186 Best Fitness: -8.81884 Best program: f1(x)=abs(sin((523.732*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.53/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 187 Best Fitness: -8.81884 Best program: f1(x)=abs(sin((523.732*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.53/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 188 Best Fitness: -8.81883 Best program: f1(x)=abs(sin(((-523.739)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.53/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 189 Best Fitness: -8.81883 Best program: f1(x)=abs(sin(((-523.739)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.53/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 190 Best Fitness: -8.81883 Best program: f1(x)=abs(sin(((-523.739)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.53/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 191 Best Fitness: -8.81883 Best program: f1(x)=abs(sin(((-523.739)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.53/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 192 Best Fitness: -8.81883 Best program: f1(x)=abs(sin(((-523.739)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.53/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 193 Best Fitness: -8.81883 Best program: f1(x)=abs(sin(((-523.739)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.53/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 194 Best Fitness: -8.81883 Best program: f1(x)=abs(sin(((-523.739)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.53/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 195 Best Fitness: -8.81883 Best program: f1(x)=abs(sin(((-523.739)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.53/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 196 Best Fitness: -8.81883 Best program: f1(x)=abs(sin(((-523.739)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.53/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 197 Best Fitness: -8.81883 Best program: f1(x)=abs(sin(((-523.739)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.53/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 198 Best Fitness: -8.81883 Best program: f1(x)=abs(sin(((-523.739)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.53/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 199 Best Fitness: -8.81883 Best program: f1(x)=abs(sin(((-523.739)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.53/(-9.89))*x29+((-9.93)/(-048.005))*x22)) Iteration: 200 Best Fitness: -8.81883 Best program: f1(x)=abs(sin(((-523.739)*x10))) f2(x)=(x13-((-47.9)/3.104)*x29+51.0*x25+abs(x27+x21+(7.53/(-9.89))*x29+((-9.93)/(-048.005))*x22)) ** CONFUSION MATRIX ** Number of classes: 2 18 1 3 35 PRECISION 0.9146825397 RECALL: 0.9342105263 train[0]=9.8839521344 test[0]=2.927627 class[0]=7.017544% ** CONFUSION MATRIX ** Number of classes: 2 18 1 3 35 PRECISION 0.9146825397 RECALL: 0.9342105263 train[1]=8.8353828760 test[1]=3.163888 class[1]=7.017544% ** CONFUSION MATRIX ** Number of classes: 2 18 1 3 35 PRECISION 0.9146825397 RECALL: 0.9342105263 train[2]=8.8353828760 test[2]=3.163888 class[2]=7.017544% ** CONFUSION MATRIX ** Number of classes: 2 18 1 3 35 PRECISION 0.9146825397 RECALL: 0.9342105263 train[3]=8.8258745442 test[3]=3.089131 class[3]=7.017544% ** CONFUSION MATRIX ** Number of classes: 2 18 1 3 35 PRECISION 0.9146825397 RECALL: 0.9342105263 train[4]=9.6675959521 test[4]=3.049988 class[4]=7.017544% ** CONFUSION MATRIX ** Number of classes: 2 18 1 3 35 PRECISION 0.9146825397 RECALL: 0.9342105263 train[5]=9.7306803872 test[5]=3.026100 class[5]=7.017544% ** CONFUSION MATRIX ** Number of classes: 2 18 1 3 35 PRECISION 0.9146825397 RECALL: 0.9342105263 train[6]=8.8353828760 test[6]=3.163888 class[6]=7.017544% ** CONFUSION MATRIX ** Number of classes: 2 18 1 3 35 PRECISION 0.9146825397 RECALL: 0.9342105263 train[7]=8.8203257366 test[7]=3.137859 class[7]=7.017544% ** CONFUSION MATRIX ** Number of classes: 2 18 1 3 35 PRECISION 0.9146825397 RECALL: 0.9342105263 train[8]=8.8203257366 test[8]=3.137859 class[8]=7.017544% ** CONFUSION MATRIX ** Number of classes: 2 18 1 3 35 PRECISION 0.9146825397 RECALL: 0.9342105263 train[9]=8.8223797243 test[9]=3.124223 class[9]=7.017544% ** CONFUSION MATRIX ** Number of classes: 2 18 1 3 35 PRECISION 0.9146825397 RECALL: 0.9342105263 train[10]=8.8250463919 test[10]=3.098320 class[10]=7.017544% ** CONFUSION MATRIX ** Number of classes: 2 18 1 3 35 PRECISION 0.9146825397 RECALL: 0.9342105263 train[11]=8.8861775756 test[11]=3.117832 class[11]=7.017544% ** CONFUSION MATRIX ** Number of classes: 2 18 1 3 35 PRECISION 0.9146825397 RECALL: 0.9342105263 train[12]=8.8203257366 test[12]=3.137859 class[12]=7.017544% ** CONFUSION MATRIX ** Number of classes: 2 18 1 3 35 PRECISION 0.9146825397 RECALL: 0.9342105263 train[13]=8.9031442780 test[13]=3.155036 class[13]=7.017544% ** CONFUSION MATRIX ** Number of classes: 2 18 1 3 35 PRECISION 0.9146825397 RECALL: 0.9342105263 train[14]=8.8237981733 test[14]=3.141708 class[14]=7.017544% ** CONFUSION MATRIX ** Number of classes: 2 18 1 3 35 PRECISION 0.9146825397 RECALL: 0.9342105263 train[15]=9.7306803872 test[15]=3.026100 class[15]=7.017544% ** CONFUSION MATRIX ** Number of classes: 2 18 1 3 35 PRECISION 0.9146825397 RECALL: 0.9342105263 train[16]=8.8237981733 test[16]=3.141708 class[16]=7.017544% ** CONFUSION MATRIX ** Number of classes: 2 18 1 3 35 PRECISION 0.9146825397 RECALL: 0.9342105263 train[17]=9.8799238193 test[17]=3.042663 class[17]=7.017544% ** CONFUSION MATRIX ** Number of classes: 2 18 1 3 35 PRECISION 0.9146825397 RECALL: 0.9342105263 train[18]=9.8260182391 test[18]=2.749506 class[18]=7.017544% ** CONFUSION MATRIX ** Number of classes: 2 18 1 3 35 PRECISION 0.9146825397 RECALL: 0.9342105263 train[19]=8.8229698997 test[19]=3.128451 class[19]=7.017544% ** CONFUSION MATRIX ** Number of classes: 2 18 1 3 35 PRECISION 0.9146825397 RECALL: 0.9342105263 train[20]=9.6675959521 test[20]=3.049988 class[20]=7.017544% ** CONFUSION MATRIX ** Number of classes: 2 18 1 3 35 PRECISION 0.9146825397 RECALL: 0.9342105263 train[21]=8.8203257366 test[21]=3.137859 class[21]=7.017544% ** CONFUSION MATRIX ** Number of classes: 2 18 1 3 35 PRECISION 0.9146825397 RECALL: 0.9342105263 train[22]=8.8203257366 test[22]=3.137859 class[22]=7.017544% ** CONFUSION MATRIX ** Number of classes: 2 18 1 3 35 PRECISION 0.9146825397 RECALL: 0.9342105263 train[23]=9.8260182391 test[23]=2.749506 class[23]=7.017544% ** CONFUSION MATRIX ** Number of classes: 2 18 1 3 35 PRECISION 0.9146825397 RECALL: 0.9342105263 train[24]=8.8353828760 test[24]=3.163888 class[24]=7.017544% ** CONFUSION MATRIX ** Number of classes: 2 18 1 3 35 PRECISION 0.9146825397 RECALL: 0.9342105263 train[25]=9.6294389490 test[25]=2.791986 class[25]=7.017544% ** CONFUSION MATRIX ** Number of classes: 2 18 1 3 35 PRECISION 0.9146825397 RECALL: 0.9342105263 train[26]=8.8181161043 test[26]=3.106115 class[26]=7.017544% ** CONFUSION MATRIX ** Number of classes: 2 18 1 3 35 PRECISION 0.9146825397 RECALL: 0.9342105263 train[27]=8.8258745442 test[27]=3.089131 class[27]=7.017544% ** CONFUSION MATRIX ** Number of classes: 2 18 1 3 35 PRECISION 0.9146825397 RECALL: 0.9342105263 train[28]=8.8203257366 test[28]=3.137859 class[28]=7.017544% ** CONFUSION MATRIX ** Number of classes: 2 18 1 3 35 PRECISION 0.9146825397 RECALL: 0.9342105263 train[29]=9.6949042189 test[29]=2.960542 class[29]=7.017544% ============================================================================= AVERAGES (TRAIN, TEST, CLASS): 9.1392491 3.068279 7.0175439% AVERAGES (PRECISION, RECALL): 0.91468254 0.93421053
# Settings Tab: Advanced # Run Date: 03/09/2025 18:41:17 # Run Time: 00:57:41 (3461274 ms) @ Parameters: ============================================================ 1. Dataset File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/datasets/MLDATASETS-master/QFC_GUI_datasets/Classification/wdbc.data 2. Dataset Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.train 3. Dataset Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.test 4. Export Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.train 5. Export Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.test 6. Export Cpp File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.cpp 7. Features: 2 8. Feature Create Model: RBF 9. Feature Evaluate Model: Neural 10. Test Iters: 30 11. Random Seed: 1 12. Threads: 1 13. GE Chromosomes: 500 14. GE Max Generations: 200 15. GE Selection Rate: 0,10 16. GE Mutation Rate: 0,05 17. GE Length: 40 18. GE Local Search Generations: 20 19. GE Local Search Rate: 0,05 20. GE Local Search Method: Crossover 21. GE Method: Genetic 22. RBF Weights: 10 23. Neural Weights: 10 24. Neural Training Method: Genetic 25. BFGS Iteration: 2001 26. GN Chromosomes: 500 27. GN Max Generations: 200 28. GN Selection Rate: 0,10 29. GN Mutation Rate: 0,05 30. GN Local Search Rate: 0,00 31. GN Local Search Method: BFGS @ Results: =============================================================== Iteration: 1 Best Fitness: -20.0994 Best program: f1(x)=((-072.10)/(-892.868))*x13 f2(x)=(85.39*x8+(x29*(x13-((-6.6)*x24)))) Iteration: 2 Best Fitness: -16.9829 Best program: f1(x)=(x25*((-85.26)*x24)) f2(x)=abs(x1+x3+((-1.739)/260.47)*x2) Iteration: 3 Best Fitness: -16.1823 Best program: f1(x)=(x25*((-85.26)*x24)) f2(x)=(85.39*x8+(x29*(x13-((-6.6)*x24)))) Iteration: 4 Best Fitness: -13.7919 Best program: f1(x)=x23+x22+((-451.887)/(-5.6))*x29 f2(x)=(x21+((-59.5)*x7)) Iteration: 5 Best Fitness: -13.7919 Best program: f1(x)=x23+x22+((-451.887)/(-5.6))*x29 f2(x)=(x21+((-59.5)*x7)) Iteration: 6 Best Fitness: -12.9385 Best program: f1(x)=x23+x22+((-456.887)/(-5.6))*x29 f2(x)=((-117.006)*x28) Iteration: 7 Best Fitness: -12.5704 Best program: f1(x)=x23+x22+((-451.887)/(-5.6))*x29 f2(x)=((-134.004)*x28+(x1+((-1.793)/635.584)*x4)) Iteration: 8 Best Fitness: -12.5704 Best program: f1(x)=x23+x22+((-451.887)/(-5.6))*x29 f2(x)=((-134.004)*x28+(x1+((-1.793)/635.584)*x4)) Iteration: 9 Best Fitness: -12.1062 Best program: f1(x)=x23+x22+((-651.827)/(-5.6))*x29+((-888.07)/3.44)*x5+582.1*x5 f2(x)=((-117.006)*x28) Iteration: 10 Best Fitness: -12.1062 Best program: f1(x)=x23+x22+((-651.827)/(-5.6))*x29+((-888.07)/3.44)*x5+582.1*x5 f2(x)=((-117.006)*x28) Iteration: 11 Best Fitness: -11.3885 Best program: f1(x)=x23+x22+((-651.827)/(-9.6))*x29+((-888.07)/3.44)*x5+582.1*x5 f2(x)=((-117.006)*x28) Iteration: 12 Best Fitness: -11.3883 Best program: f1(x)=x23+x22+((-251.827)/(-3.7))*x29+((-888.09)/3.44)*x5+582.1*x5 f2(x)=((-117.006)*x28) Iteration: 13 Best Fitness: -11.2926 Best program: f1(x)=x23+x22+((-551.817)/(-5.6))*x29+((-881.07)/6.48)*x6+582.1*x5 f2(x)=((-117.006)*x28) Iteration: 14 Best Fitness: -11.0459 Best program: f1(x)=x23+x22+((-551.847)/(-9.6))*x29+((-888.07)/6.34)*x5+582.1*x5 f2(x)=((-1.740)*x11) Iteration: 15 Best Fitness: -11.0214 Best program: f1(x)=x23+x22+((-651.827)/(-9.6))*x29+((-888.07)/6.34)*x5+582.1*x5 f2(x)=((-1.740)*x11) Iteration: 16 Best Fitness: -10.2977 Best program: f1(x)=x23+x22+((-256.847)/(-9.6))*x29+((-888.0)/(-4.93))*x25+((-0.82)/4.32)*x26+x6+73.6*x7 f2(x)=((-1.740)*x11) Iteration: 17 Best Fitness: -10.2906 Best program: f1(x)=x23+x22+((-256.847)/(-9.6))*x29+((-888.0)/(-4.93))*x25+((-0.82)/4.32)*x26+x6+73.6*x7 f2(x)=((-1.740)*x19) Iteration: 18 Best Fitness: -9.43994 Best program: f1(x)=x23+x22+((-656.847)/(-9.6))*x29+((-888.0)/(-4.93))*x25+((-0.82)/4.36)*x26+x6+73.6*x7 f2(x)=((-1.740)*x11) Iteration: 19 Best Fitness: -9.36583 Best program: f1(x)=x23+x22+((-656.847)/(-9.6))*x29+((-888.4)/(-4.93))*x25+((-0.82)/4.36)*x26+x6+73.6*x7 f2(x)=((-1.740)*x19) Iteration: 20 Best Fitness: -9.08013 Best program: f1(x)=x23+x22+((-256.847)/(-2.6))*x29+((-888.0)/(-4.93))*x25+((-75.2)/4.32)*x26+x6+73.6*x7 f2(x)=((-1.7)*x15) Iteration: 21 Best Fitness: -8.97269 Best program: f1(x)=x23+x22+((-656.847)/(-9.6))*x29+((-888.9)/(-4.93))*x25+((-75.2)/4.36)*x26+x6+73.6*x7 f2(x)=((-1.7)*x15) Iteration: 22 Best Fitness: -8.92298 Best program: f1(x)=x23+x22+((-256.847)/(-2.6))*x29+((-888.0)/(-4.23))*x25+((-75.2)/4.32)*x26+x6+73.7*x7 f2(x)=((-1.7)*x15) Iteration: 23 Best Fitness: -8.67985 Best program: f1(x)=x23+x22+((-656.847)/(-9.6))*x29+((-888.0)/(-4.23))*x25+((-75.2)/4.36)*x26+x6+73.6*x7 f2(x)=((-1.7)*x15) Iteration: 24 Best Fitness: -8.29583 Best program: f1(x)=x23+x22+((-656.847)/(-9.6))*x29+((-888.9)/(-2.93))*x25+((-75.2)/4.36)*x26+x6+73.9*x7 f2(x)=((-1.7)*x15) Iteration: 25 Best Fitness: -8.29471 Best program: f1(x)=x23+x22+((-656.847)/(-9.6))*x29+((-888.9)/(-2.93))*x25+((-75.2)/4.36)*x26+x6+77.6*x7 f2(x)=((-1.7)*x15) Iteration: 26 Best Fitness: -8.29054 Best program: f1(x)=x23+x22+((-656.847)/(-9.6))*x29+((-888.9)/(-2.93))*x25+((-75.2)/4.36)*x26+x6+93.9*x7 f2(x)=((-1.7)*x15) Iteration: 27 Best Fitness: -8.27666 Best program: f1(x)=x23+x22+((-656.847)/(-9.6))*x29+((-888.9)/(-2.93))*x25+((-75.2)/4.36)*x26+x9+77.6*x7 f2(x)=((-1.7)*x15) Iteration: 28 Best Fitness: -8.26604 Best program: f1(x)=x23+x22+((-656.847)/(-9.6))*x29+((-888.9)/(-2.93))*x25+((-75.2)/4.36)*x26+x12+73.9*x7 f2(x)=(1.7*x15) Iteration: 29 Best Fitness: -8.26389 Best program: f1(x)=x23+x22+((-656.847)/(-9.6))*x29+((-888.4)/(-2.93))*x25+((-75.2)/4.36)*x26+x12+77.6*x7 f2(x)=((-1.7)*x15) Iteration: 30 Best Fitness: -8.25099 Best program: f1(x)=x23+x22+((-556.847)/(-9.6))*x29+((-888.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x12+77.6*x7 f2(x)=((-1.7)*x15) Iteration: 31 Best Fitness: -8.25099 Best program: f1(x)=x23+x22+((-556.847)/(-9.6))*x29+((-888.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x12+77.6*x7 f2(x)=((-3.7)*x15) Iteration: 32 Best Fitness: -8.24977 Best program: f1(x)=x23+x22+((-556.847)/(-9.6))*x29+((-848.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x12+93.9*x7 f2(x)=(1.7*x15) Iteration: 33 Best Fitness: -8.24976 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-848.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x12+93.9*x7 f2(x)=(1.746*x19) Iteration: 34 Best Fitness: -8.23768 Best program: f1(x)=x23+x22+((-556.847)/(-9.6))*x29+((-848.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x12+93.9*x7 f2(x)=((-1.7)*x15) Iteration: 35 Best Fitness: -8.23025 Best program: f1(x)=x23+x22+((-556.847)/(-9.6))*x29+((-848.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(1.7*x15) Iteration: 36 Best Fitness: -8.23025 Best program: f1(x)=x23+x22+((-556.847)/(-9.6))*x29+((-848.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(1.7*x15) Iteration: 37 Best Fitness: -8.22954 Best program: f1(x)=x23+x22+((-556.847)/(-9.6))*x29+((-848.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(1.74*x7) Iteration: 38 Best Fitness: -8.22954 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-848.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(1.74*x7) Iteration: 39 Best Fitness: -8.22954 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-848.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(1.74*x7) Iteration: 40 Best Fitness: -8.22741 Best program: f1(x)=x23+x22+((-556.847)/(-9.6))*x29+((-848.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(3.74*x7) Iteration: 41 Best Fitness: -8.22328 Best program: f1(x)=x23+x22+((-556.847)/(-9.6))*x29+((-848.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(16.46*x19) Iteration: 42 Best Fitness: -8.22328 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-848.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(16.46*x19) Iteration: 43 Best Fitness: -8.22003 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-848.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(2.74*x27) Iteration: 44 Best Fitness: -8.22003 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-848.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(2.74*x27) Iteration: 45 Best Fitness: -8.21572 Best program: f1(x)=x23+x22+((-556.847)/(-9.6))*x29+((-848.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(26.46*x19) Iteration: 46 Best Fitness: -8.21572 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-848.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(26.46*x19) Iteration: 47 Best Fitness: -8.21493 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-848.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.6*x7 f2(x)=(4.74*x27) Iteration: 48 Best Fitness: -8.2145 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-848.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(28.46*x19) Iteration: 49 Best Fitness: -8.2145 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-848.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(28.46*x19) Iteration: 50 Best Fitness: -8.2145 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-848.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(28.46*x19) Iteration: 51 Best Fitness: -8.2145 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-848.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(28.46*x19) Iteration: 52 Best Fitness: -8.21448 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-848.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(28.49*x19) Iteration: 53 Best Fitness: -8.21448 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-848.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(28.49*x19) Iteration: 54 Best Fitness: -8.21448 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-848.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(28.49*x19) Iteration: 55 Best Fitness: -8.21448 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-848.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(28.49*x19) Iteration: 56 Best Fitness: -8.21448 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-848.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(28.49*x19) Iteration: 57 Best Fitness: -8.21448 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-848.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(28.49*x19) Iteration: 58 Best Fitness: -8.21448 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-848.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(28.49*x19) Iteration: 59 Best Fitness: -8.21448 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-848.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(28.49*x19) Iteration: 60 Best Fitness: -8.21448 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-848.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(28.49*x19) Iteration: 61 Best Fitness: -8.21422 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-849.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(28.49*x19) Iteration: 62 Best Fitness: -8.21422 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-849.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(28.49*x19) Iteration: 63 Best Fitness: -8.214 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-849.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(28.89*x19) Iteration: 64 Best Fitness: -8.20927 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-849.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(23.89*x19*(-00.936)*x26+((-263.8)/187.473)*x26) Iteration: 65 Best Fitness: -8.20927 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-849.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(23.89*x19*(-00.936)*x26+((-263.8)/187.473)*x26) Iteration: 66 Best Fitness: -8.20927 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-849.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(23.89*x19*(-00.936)*x26+((-263.8)/187.473)*x26) Iteration: 67 Best Fitness: -8.20927 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-849.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(23.89*x19*(-00.936)*x26+((-263.8)/187.473)*x26) Iteration: 68 Best Fitness: -8.20835 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-849.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(23.89*x19*(-00.936)*x26+((-273.8)/187.473)*x26) Iteration: 69 Best Fitness: -8.2079 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-849.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(23.89*x19*(-00.936)*x26+((-263.8)/177.473)*x26) Iteration: 70 Best Fitness: -8.2079 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-849.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(23.89*x19*(-00.936)*x26+((-263.8)/177.473)*x26) Iteration: 71 Best Fitness: -8.18633 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-849.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(23.89*x19*(-05.936)*x26+((-205.865)/(-784.35))*x26) Iteration: 72 Best Fitness: -8.17101 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-849.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(25.89*x19*(-00.936)*x26+((-673.8)/184.473)*x26) Iteration: 73 Best Fitness: -8.17101 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-849.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(25.89*x19*(-00.936)*x26+((-673.8)/184.473)*x26) Iteration: 74 Best Fitness: -8.17101 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-849.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x15+93.1*x7 f2(x)=(25.89*x19*(-00.936)*x26+((-673.8)/184.473)*x26) Iteration: 75 Best Fitness: -8.16956 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-849.7)/(-2.74))*x25+((-75.2)/4.35)*x26+x9+93.5*x7 f2(x)=(23.89*x19*(-01.936)*x26+((-273.8)/087.473)*x26) Iteration: 76 Best Fitness: -8.16445 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-849.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(23.89*x19*(-01.936)*x26+((-673.8)/184.473)*x26) Iteration: 77 Best Fitness: -8.16445 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-849.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(23.89*x19*(-01.936)*x26+((-673.8)/184.473)*x26) Iteration: 78 Best Fitness: -8.16217 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-849.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(23.89*x19*(-01.936)*x26+((-673.8)/184.473)*x26) Iteration: 79 Best Fitness: -8.16131 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-849.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.936)*x26+((-273.8)/087.473)*x26) Iteration: 80 Best Fitness: -8.16131 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-849.9)/(-2.93))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.936)*x26+((-273.8)/087.473)*x26) Iteration: 81 Best Fitness: -8.16125 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(23.82*x19*(-01.936)*x26+((-673.8)/174.473)*x26) Iteration: 82 Best Fitness: -8.16125 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(23.82*x19*(-01.936)*x26+((-673.8)/174.473)*x26) Iteration: 83 Best Fitness: -8.16125 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(23.82*x19*(-01.936)*x26+((-673.8)/174.473)*x26) Iteration: 84 Best Fitness: -8.16125 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(23.82*x19*(-01.936)*x26+((-673.8)/174.473)*x26) Iteration: 85 Best Fitness: -8.16125 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(23.82*x19*(-01.936)*x26+((-673.8)/174.473)*x26) Iteration: 86 Best Fitness: -8.16105 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.936)*x26+((-673.8)/174.473)*x26) Iteration: 87 Best Fitness: -8.16105 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.936)*x26+((-673.8)/174.473)*x26) Iteration: 88 Best Fitness: -8.16098 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.936)*x26+((-655.4)/176.473)*x26) Iteration: 89 Best Fitness: -8.16096 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.936)*x26+((-663.8)/177.473)*x26) Iteration: 90 Best Fitness: -8.16096 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.936)*x26+((-655.8)/174.473)*x26) Iteration: 91 Best Fitness: -8.16096 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.936)*x26+((-655.8)/174.473)*x26) Iteration: 92 Best Fitness: -8.16091 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-897.7)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.966)*x26+((-653.4)/174.473)*x26) Iteration: 93 Best Fitness: -8.16091 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-897.7)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.966)*x26+((-653.4)/174.473)*x26) Iteration: 94 Best Fitness: -8.16091 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-897.7)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.966)*x26+((-663.8)/177.473)*x26) Iteration: 95 Best Fitness: -8.16091 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-897.7)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.966)*x26+((-663.8)/177.473)*x26) Iteration: 96 Best Fitness: -8.16091 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-897.7)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.966)*x26+((-663.8)/177.473)*x26) Iteration: 97 Best Fitness: -8.16091 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-897.7)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.966)*x26+((-663.8)/177.473)*x26) Iteration: 98 Best Fitness: -8.16091 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-897.7)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.967)*x26+((-655.4)/176.173)*x26) Iteration: 99 Best Fitness: -8.16091 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-897.7)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.967)*x26+((-655.8)/176.173)*x26) Iteration: 100 Best Fitness: -8.1609 Best program: f1(x)=x23+x22+((-556.867)/(-9.6))*x29+((-897.4)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.966)*x26+((-663.8)/177.473)*x26) Iteration: 101 Best Fitness: -8.15854 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.7)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.966)*x26+((-653.4)/184.473)*x26) Iteration: 102 Best Fitness: -8.15854 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.7)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.966)*x26+((-653.4)/184.473)*x26) Iteration: 103 Best Fitness: -8.15809 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.7)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.966)*x26+((-653.4)/174.473)*x26) Iteration: 104 Best Fitness: -8.15809 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.7)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.966)*x26+((-653.4)/174.473)*x26) Iteration: 105 Best Fitness: -8.15809 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.7)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.966)*x26+((-653.4)/174.473)*x26) Iteration: 106 Best Fitness: -8.15809 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.7)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.966)*x26+((-653.4)/174.473)*x26) Iteration: 107 Best Fitness: -8.15809 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.7)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.966)*x26+((-653.4)/174.473)*x26) Iteration: 108 Best Fitness: -8.15809 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.7)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.966)*x26+((-653.4)/174.473)*x26) Iteration: 109 Best Fitness: -8.15809 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.7)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.966)*x26+((-653.4)/174.473)*x26) Iteration: 110 Best Fitness: -8.15808 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.7)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.967)*x26+((-658.4)/174.573)*x26) Iteration: 111 Best Fitness: -8.15808 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.7)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.967)*x26+((-658.4)/174.573)*x26) Iteration: 112 Best Fitness: -8.15808 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.7)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.966)*x26+((-668.8)/177.473)*x26) Iteration: 113 Best Fitness: -8.15808 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.966)*x26+((-658.4)/174.573)*x26) Iteration: 114 Best Fitness: -8.15808 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.966)*x26+((-665.4)/176.453)*x26) Iteration: 115 Best Fitness: -8.15808 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.966)*x26+((-665.4)/176.453)*x26) Iteration: 116 Best Fitness: -8.15807 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.7)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.39*x19*(-01.966)*x26+((-668.8)/177.473)*x26) Iteration: 117 Best Fitness: -8.15806 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.7)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.39*x19*(-01.966)*x26+((-668.4)/176.173)*x26) Iteration: 118 Best Fitness: -8.15806 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.7)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.39*x19*(-01.966)*x26+((-668.4)/176.173)*x26) Iteration: 119 Best Fitness: -8.15806 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.7)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.39*x19*(-01.966)*x26+((-668.4)/176.173)*x26) Iteration: 120 Best Fitness: -8.15805 Best program: f1(x)=x23+x22+((-550.827)/(-9.6))*x29+((-897.7)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.765)*x26+((-678.4)/174.573)*x26) Iteration: 121 Best Fitness: -8.15805 Best program: f1(x)=x23+x22+((-550.865)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.765)*x26+((-678.4)/174.573)*x26) Iteration: 122 Best Fitness: -8.15805 Best program: f1(x)=x23+x22+((-550.865)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.765)*x26+((-678.4)/174.573)*x26) Iteration: 123 Best Fitness: -8.15804 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.7)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.765)*x26+((-678.4)/174.403)*x26) Iteration: 124 Best Fitness: -8.15804 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.7)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.765)*x26+((-678.4)/174.403)*x26) Iteration: 125 Best Fitness: -8.15804 Best program: f1(x)=x23+x22+((-550.865)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.765)*x26+((-678.4)/174.373)*x26) Iteration: 126 Best Fitness: -8.15804 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.765)*x26+((-678.4)/174.173)*x26) Iteration: 127 Best Fitness: -8.15804 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.765)*x26+((-678.4)/174.173)*x26) Iteration: 128 Best Fitness: -8.15804 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.765)*x26+((-678.4)/174.173)*x26) Iteration: 129 Best Fitness: -8.15804 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.765)*x26+((-678.4)/174.173)*x26) Iteration: 130 Best Fitness: -8.15804 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.765)*x26+((-678.4)/174.173)*x26) Iteration: 131 Best Fitness: -8.15804 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.765)*x26+((-678.4)/174.173)*x26) Iteration: 132 Best Fitness: -8.15804 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.765)*x26+((-678.4)/174.173)*x26) Iteration: 133 Best Fitness: -8.15804 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.765)*x26+((-678.4)/174.173)*x26) Iteration: 134 Best Fitness: -8.15804 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.765)*x26+((-678.4)/174.173)*x26) Iteration: 135 Best Fitness: -8.15804 Best program: f1(x)=x23+x22+((-550.865)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.765)*x26+((-678.7)/174.173)*x26) Iteration: 136 Best Fitness: -8.15804 Best program: f1(x)=x23+x22+((-550.865)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.765)*x26+((-678.7)/174.173)*x26) Iteration: 137 Best Fitness: -8.15804 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.765)*x26+((-678.7)/174.173)*x26) Iteration: 138 Best Fitness: -8.15804 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.765)*x26+((-678.7)/174.173)*x26) Iteration: 139 Best Fitness: -8.15804 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.765)*x26+((-678.7)/174.173)*x26) Iteration: 140 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.765)*x26+((-679.8)/174.173)*x26) Iteration: 141 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.765)*x26+((-679.8)/174.173)*x26) Iteration: 142 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.765)*x26+((-679.8)/174.173)*x26) Iteration: 143 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.765)*x26+((-679.8)/174.173)*x26) Iteration: 144 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.765)*x26+((-679.8)/174.173)*x26) Iteration: 145 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.173)*x26) Iteration: 146 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.173)*x26) Iteration: 147 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.173)*x26) Iteration: 148 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.173)*x26) Iteration: 149 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.173)*x26) Iteration: 150 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.173)*x26) Iteration: 151 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.173)*x26) Iteration: 152 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.173)*x26) Iteration: 153 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.173)*x26) Iteration: 154 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.173)*x26) Iteration: 155 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.173)*x26) Iteration: 156 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.173)*x26) Iteration: 157 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.173)*x26) Iteration: 158 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.173)*x26) Iteration: 159 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.173)*x26) Iteration: 160 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.113)*x26) Iteration: 161 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.113)*x26) Iteration: 162 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.103)*x26) Iteration: 163 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.103)*x26) Iteration: 164 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.103)*x26) Iteration: 165 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.103)*x26) Iteration: 166 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.103)*x26) Iteration: 167 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.103)*x26) Iteration: 168 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.103)*x26) Iteration: 169 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.103)*x26) Iteration: 170 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.103)*x26) Iteration: 171 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.103)*x26) Iteration: 172 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.103)*x26) Iteration: 173 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.103)*x26) Iteration: 174 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.103)*x26) Iteration: 175 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.103)*x26) Iteration: 176 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.103)*x26) Iteration: 177 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.103)*x26) Iteration: 178 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.103)*x26) Iteration: 179 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.103)*x26) Iteration: 180 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.8)/174.103)*x26) Iteration: 181 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.9)/174.103)*x26) Iteration: 182 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.9)/174.103)*x26) Iteration: 183 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.9)/174.103)*x26) Iteration: 184 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.9)/174.103)*x26) Iteration: 185 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.9)/174.103)*x26) Iteration: 186 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.9)/174.103)*x26) Iteration: 187 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.9)/174.103)*x26) Iteration: 188 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.9)/174.103)*x26) Iteration: 189 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.9)/174.103)*x26) Iteration: 190 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.9)/174.103)*x26) Iteration: 191 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.9)/174.103)*x26) Iteration: 192 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.9)/174.103)*x26) Iteration: 193 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.9)/174.103)*x26) Iteration: 194 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.9)/174.103)*x26) Iteration: 195 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.9)/174.103)*x26) Iteration: 196 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.9)/174.103)*x26) Iteration: 197 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.9)/174.103)*x26) Iteration: 198 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.9)/174.103)*x26) Iteration: 199 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.9)/174.103)*x26) Iteration: 200 Best Fitness: -8.15803 Best program: f1(x)=x23+x22+((-550.867)/(-9.6))*x29+((-897.9)/(-2.94))*x25+((-75.2)/4.35)*x26+x9+93.1*x7 f2(x)=(25.89*x19*(-01.767)*x26+((-679.9)/174.103)*x26) ** CONFUSION MATRIX ** Number of classes: 2 19 0 2 36 PRECISION 0.9523809524 RECALL: 0.9736842105 train[0]=21.3790622691 test[0]=3.680159 class[0]=3.508772% ** CONFUSION MATRIX ** Number of classes: 2 19 0 2 36 PRECISION 0.9523809524 RECALL: 0.9736842105 train[1]=21.3790622691 test[1]=3.680159 class[1]=3.508772% ** CONFUSION MATRIX ** Number of classes: 2 19 0 2 36 PRECISION 0.9523809524 RECALL: 0.9736842105 train[2]=21.3790622691 test[2]=3.680159 class[2]=3.508772% ** CONFUSION MATRIX ** Number of classes: 2 19 0 2 36 PRECISION 0.9523809524 RECALL: 0.9736842105 train[3]=21.3790622691 test[3]=3.680159 class[3]=3.508772% ** CONFUSION MATRIX ** Number of classes: 2 19 0 2 36 PRECISION 0.9523809524 RECALL: 0.9736842105 train[4]=21.3790622691 test[4]=3.680159 class[4]=3.508772% ** CONFUSION MATRIX ** Number of classes: 2 19 0 2 36 PRECISION 0.9523809524 RECALL: 0.9736842105 train[5]=21.3790622691 test[5]=3.680159 class[5]=3.508772% ** CONFUSION MATRIX ** Number of classes: 2 19 0 2 36 PRECISION 0.9523809524 RECALL: 0.9736842105 train[6]=21.3790622691 test[6]=3.680159 class[6]=3.508772% ** CONFUSION MATRIX ** Number of classes: 2 19 0 2 36 PRECISION 0.9523809524 RECALL: 0.9736842105 train[7]=21.3790622691 test[7]=3.680159 class[7]=3.508772% ** CONFUSION MATRIX ** Number of classes: 2 19 0 2 36 PRECISION 0.9523809524 RECALL: 0.9736842105 train[8]=21.3790622691 test[8]=3.680159 class[8]=3.508772% ** CONFUSION MATRIX ** Number of classes: 2 19 0 2 36 PRECISION 0.9523809524 RECALL: 0.9736842105 train[9]=21.3790622691 test[9]=3.680159 class[9]=3.508772% ** CONFUSION MATRIX ** Number of classes: 2 19 0 2 36 PRECISION 0.9523809524 RECALL: 0.9736842105 train[10]=21.3790622691 test[10]=3.680159 class[10]=3.508772% ** CONFUSION MATRIX ** Number of classes: 2 19 0 2 36 PRECISION 0.9523809524 RECALL: 0.9736842105 train[11]=21.3790622691 test[11]=3.680159 class[11]=3.508772% ** CONFUSION MATRIX ** Number of classes: 2 19 0 2 36 PRECISION 0.9523809524 RECALL: 0.9736842105 train[12]=21.3790622691 test[12]=3.680159 class[12]=3.508772% ** CONFUSION MATRIX ** Number of classes: 2 19 0 2 36 PRECISION 0.9523809524 RECALL: 0.9736842105 train[13]=21.3790622691 test[13]=3.680159 class[13]=3.508772% ** CONFUSION MATRIX ** Number of classes: 2 19 0 2 36 PRECISION 0.9523809524 RECALL: 0.9736842105 train[14]=21.3790622691 test[14]=3.680159 class[14]=3.508772% ** CONFUSION MATRIX ** Number of classes: 2 19 0 2 36 PRECISION 0.9523809524 RECALL: 0.9736842105 train[15]=21.3790622691 test[15]=3.680159 class[15]=3.508772% ** CONFUSION MATRIX ** Number of classes: 2 19 0 2 36 PRECISION 0.9523809524 RECALL: 0.9736842105 train[16]=21.3790622691 test[16]=3.680159 class[16]=3.508772% ** CONFUSION MATRIX ** Number of classes: 2 19 0 2 36 PRECISION 0.9523809524 RECALL: 0.9736842105 train[17]=21.3790622691 test[17]=3.680159 class[17]=3.508772% ** CONFUSION MATRIX ** Number of classes: 2 19 0 2 36 PRECISION 0.9523809524 RECALL: 0.9736842105 train[18]=21.3790622691 test[18]=3.680159 class[18]=3.508772% ** CONFUSION MATRIX ** Number of classes: 2 19 0 2 36 PRECISION 0.9523809524 RECALL: 0.9736842105 train[19]=21.3790622691 test[19]=3.680159 class[19]=3.508772% ** CONFUSION MATRIX ** Number of classes: 2 19 0 2 36 PRECISION 0.9523809524 RECALL: 0.9736842105 train[20]=21.3790622691 test[20]=3.680159 class[20]=3.508772% ** CONFUSION MATRIX ** Number of classes: 2 19 0 2 36 PRECISION 0.9523809524 RECALL: 0.9736842105 train[21]=21.3790622691 test[21]=3.680159 class[21]=3.508772% ** CONFUSION MATRIX ** Number of classes: 2 19 0 2 36 PRECISION 0.9523809524 RECALL: 0.9736842105 train[22]=21.3790622691 test[22]=3.680159 class[22]=3.508772% ** CONFUSION MATRIX ** Number of classes: 2 19 0 2 36 PRECISION 0.9523809524 RECALL: 0.9736842105 train[23]=21.3790622691 test[23]=3.680159 class[23]=3.508772% ** CONFUSION MATRIX ** Number of classes: 2 19 0 2 36 PRECISION 0.9523809524 RECALL: 0.9736842105 train[24]=21.3790622691 test[24]=3.680159 class[24]=3.508772% ** CONFUSION MATRIX ** Number of classes: 2 19 0 2 36 PRECISION 0.9523809524 RECALL: 0.9736842105 train[25]=21.3790622691 test[25]=3.680159 class[25]=3.508772% ** CONFUSION MATRIX ** Number of classes: 2 19 0 2 36 PRECISION 0.9523809524 RECALL: 0.9736842105 train[26]=21.3790622691 test[26]=3.680159 class[26]=3.508772% ** CONFUSION MATRIX ** Number of classes: 2 19 0 2 36 PRECISION 0.9523809524 RECALL: 0.9736842105 train[27]=21.3790622691 test[27]=3.680159 class[27]=3.508772% ** CONFUSION MATRIX ** Number of classes: 2 19 0 2 36 PRECISION 0.9523809524 RECALL: 0.9736842105 train[28]=21.3790622691 test[28]=3.680159 class[28]=3.508772% ** CONFUSION MATRIX ** Number of classes: 2 19 0 2 36 PRECISION 0.9523809524 RECALL: 0.9736842105 train[29]=21.3790622691 test[29]=3.680159 class[29]=3.508772% ============================================================================= AVERAGES (TRAIN, TEST, CLASS): 21.379062 3.6801595 3.5087719% AVERAGES (PRECISION, RECALL): 0.95238095 0.97368421
# Settings Tab: Advanced # Run Date: 04/09/2025 18:05:16 # Run Time: 00:00:00 (590 ms) @ Parameters: ============================================================ 1. Dataset File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/datasets/MLDATASETS-master/QFC_GUI_datasets/Classification/heart.data 2. Dataset Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.train 3. Dataset Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.test 4. Export Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.train 5. Export Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.test 6. Export Cpp File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.cpp 7. Features: 1 8. Feature Create Model: Copy 9. Feature Evaluate Model: RBF 10. Test Iters: 30 11. Random Seed: 1 12. Threads: 1 13. RBF Weights: 10 @ Results: =============================================================== ** CONFUSION MATRIX ** Number of classes: 2 8 3 4 12 PRECISION 0.7333333333 RECALL: 0.7386363636 train[0]=45.1098941739 test[0]=5.145636 class[0]=25.925926% ** CONFUSION MATRIX ** Number of classes: 2 7 3 5 12 PRECISION 0.6916666667 RECALL: 0.7029411765 train[1]=44.1930556513 test[1]=5.056525 class[1]=29.629630% ** CONFUSION MATRIX ** Number of classes: 2 10 3 2 12 PRECISION 0.8166666667 RECALL: 0.8131868132 train[2]=46.3813658493 test[2]=4.916375 class[2]=18.518519% ** CONFUSION MATRIX ** Number of classes: 2 6 4 6 11 PRECISION 0.6166666667 RECALL: 0.6235294118 train[3]=46.1154658874 test[3]=5.482175 class[3]=37.037037% ** CONFUSION MATRIX ** Number of classes: 2 6 4 6 11 PRECISION 0.6166666667 RECALL: 0.6235294118 train[4]=46.4527485070 test[4]=5.612704 class[4]=37.037037% ** CONFUSION MATRIX ** Number of classes: 2 7 4 5 11 PRECISION 0.6583333333 RECALL: 0.6619318182 train[5]=45.0898788502 test[5]=5.110384 class[5]=33.333333% ** CONFUSION MATRIX ** Number of classes: 2 7 3 5 12 PRECISION 0.6916666667 RECALL: 0.7029411765 train[6]=44.4375935412 test[6]=4.956903 class[6]=29.629630% ** CONFUSION MATRIX ** Number of classes: 2 7 2 5 13 PRECISION 0.7250000000 RECALL: 0.7500000000 train[7]=44.5854903878 test[7]=4.938638 class[7]=25.925926% ** CONFUSION MATRIX ** Number of classes: 2 6 3 6 12 PRECISION 0.6500000000 RECALL: 0.6666666667 train[8]=44.9847418741 test[8]=5.087949 class[8]=33.333333% ** CONFUSION MATRIX ** Number of classes: 2 7 4 5 11 PRECISION 0.6583333333 RECALL: 0.6619318182 train[9]=44.9706619339 test[9]=5.020150 class[9]=33.333333% ** CONFUSION MATRIX ** Number of classes: 2 8 4 4 11 PRECISION 0.7000000000 RECALL: 0.7000000000 train[10]=45.9207763507 test[10]=5.443337 class[10]=29.629630% ** CONFUSION MATRIX ** Number of classes: 2 6 3 6 12 PRECISION 0.6500000000 RECALL: 0.6666666667 train[11]=44.1556460027 test[11]=5.011034 class[11]=33.333333% ** CONFUSION MATRIX ** Number of classes: 2 9 3 3 12 PRECISION 0.7750000000 RECALL: 0.7750000000 train[12]=45.7059454419 test[12]=5.350810 class[12]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 7 3 5 12 PRECISION 0.6916666667 RECALL: 0.7029411765 train[13]=44.0573882640 test[13]=5.068122 class[13]=29.629630% ** CONFUSION MATRIX ** Number of classes: 2 6 3 6 12 PRECISION 0.6500000000 RECALL: 0.6666666667 train[14]=44.1384380001 test[14]=5.292698 class[14]=33.333333% ** CONFUSION MATRIX ** Number of classes: 2 8 3 4 12 PRECISION 0.7333333333 RECALL: 0.7386363636 train[15]=45.2939429062 test[15]=4.967693 class[15]=25.925926% ** CONFUSION MATRIX ** Number of classes: 2 7 3 5 12 PRECISION 0.6916666667 RECALL: 0.7029411765 train[16]=45.3946497630 test[16]=5.340496 class[16]=29.629630% ** CONFUSION MATRIX ** Number of classes: 2 7 3 5 12 PRECISION 0.6916666667 RECALL: 0.7029411765 train[17]=44.6406110416 test[17]=5.033041 class[17]=29.629630% ** CONFUSION MATRIX ** Number of classes: 2 6 4 6 11 PRECISION 0.6166666667 RECALL: 0.6235294118 train[18]=45.9389337583 test[18]=5.319131 class[18]=37.037037% ** CONFUSION MATRIX ** Number of classes: 2 9 4 3 11 PRECISION 0.7416666667 RECALL: 0.7390109890 train[19]=46.0294805917 test[19]=5.216264 class[19]=25.925926% ** CONFUSION MATRIX ** Number of classes: 2 7 4 5 11 PRECISION 0.6583333333 RECALL: 0.6619318182 train[20]=44.3158938112 test[20]=4.998159 class[20]=33.333333% ** CONFUSION MATRIX ** Number of classes: 2 6 3 6 12 PRECISION 0.6500000000 RECALL: 0.6666666667 train[21]=44.2898341765 test[21]=4.987968 class[21]=33.333333% ** CONFUSION MATRIX ** Number of classes: 2 7 5 5 10 PRECISION 0.6250000000 RECALL: 0.6250000000 train[22]=45.4562967789 test[22]=5.355562 class[22]=37.037037% ** CONFUSION MATRIX ** Number of classes: 2 7 3 5 12 PRECISION 0.6916666667 RECALL: 0.7029411765 train[23]=45.1929998749 test[23]=5.389578 class[23]=29.629630% ** CONFUSION MATRIX ** Number of classes: 2 6 3 6 12 PRECISION 0.6500000000 RECALL: 0.6666666667 train[24]=44.2681806636 test[24]=5.145677 class[24]=33.333333% ** CONFUSION MATRIX ** Number of classes: 2 7 3 5 12 PRECISION 0.6916666667 RECALL: 0.7029411765 train[25]=44.6052267531 test[25]=4.845154 class[25]=29.629630% ** CONFUSION MATRIX ** Number of classes: 2 6 3 6 12 PRECISION 0.6500000000 RECALL: 0.6666666667 train[26]=44.5083588819 test[26]=5.367579 class[26]=33.333333% ** CONFUSION MATRIX ** Number of classes: 2 7 3 5 12 PRECISION 0.6916666667 RECALL: 0.7029411765 train[27]=44.3002632530 test[27]=5.112136 class[27]=29.629630% ** CONFUSION MATRIX ** Number of classes: 2 6 3 6 12 PRECISION 0.6500000000 RECALL: 0.6666666667 train[28]=44.1420848908 test[28]=5.057046 class[28]=33.333333% ** CONFUSION MATRIX ** Number of classes: 2 7 4 5 11 PRECISION 0.6583333333 RECALL: 0.6619318182 train[29]=45.4732807052 test[29]=5.185210 class[29]=33.333333% ============================================================================= AVERAGES (TRAIN,TEST,CLASS): 45.004971 5.1604712 30.864198% AVERAGES (PRECISION,RECALL): 0.68055556 0.6895994
# Settings Tab: Advanced # Run Date: 04/09/2025 18:08:14 # Run Time: 00:16:01 (961806 ms) @ Parameters: ============================================================ 1. Dataset File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/datasets/MLDATASETS-master/QFC_GUI_datasets/Classification/heart.data 2. Dataset Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.train 3. Dataset Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.test 4. Export Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.train 5. Export Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.test 6. Export Cpp File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.cpp 7. Features: 1 8. Feature Create Model: Copy 9. Feature Evaluate Model: Neural 10. Test Iters: 30 11. Random Seed: 1 12. Threads: 1 13. Neural Weights: 10 14. Neural Training Method: Genetic 15. BFGS Iteration: 2001 16. GN Chromosomes: 500 17. GN Max Generations: 200 18. GN Selection Rate: 0,10 19. GN Mutation Rate: 0,05 20. GN Local Search Rate: 0,00 21. GN Local Search Method: BFGS @ Results: =============================================================== ** CONFUSION MATRIX ** Number of classes: 2 10 1 2 14 PRECISION 0.8833333333 RECALL: 0.8920454545 train[0]=25.2026607333 test[0]=2.789474 class[0]=11.111111% ** CONFUSION MATRIX ** Number of classes: 2 10 1 2 14 PRECISION 0.8833333333 RECALL: 0.8920454545 train[1]=25.2026607333 test[1]=2.789474 class[1]=11.111111% ** CONFUSION MATRIX ** Number of classes: 2 10 1 2 14 PRECISION 0.8833333333 RECALL: 0.8920454545 train[2]=25.2026607333 test[2]=2.789474 class[2]=11.111111% ** CONFUSION MATRIX ** Number of classes: 2 10 1 2 14 PRECISION 0.8833333333 RECALL: 0.8920454545 train[3]=25.2026607333 test[3]=2.789474 class[3]=11.111111% ** CONFUSION MATRIX ** Number of classes: 2 10 1 2 14 PRECISION 0.8833333333 RECALL: 0.8920454545 train[4]=25.2026607333 test[4]=2.789474 class[4]=11.111111% ** CONFUSION MATRIX ** Number of classes: 2 10 1 2 14 PRECISION 0.8833333333 RECALL: 0.8920454545 train[5]=25.2026607333 test[5]=2.789474 class[5]=11.111111% ** CONFUSION MATRIX ** Number of classes: 2 10 1 2 14 PRECISION 0.8833333333 RECALL: 0.8920454545 train[6]=25.2026607333 test[6]=2.789474 class[6]=11.111111% ** CONFUSION MATRIX ** Number of classes: 2 10 1 2 14 PRECISION 0.8833333333 RECALL: 0.8920454545 train[7]=25.2026607333 test[7]=2.789474 class[7]=11.111111% ** CONFUSION MATRIX ** Number of classes: 2 10 1 2 14 PRECISION 0.8833333333 RECALL: 0.8920454545 train[8]=25.2026607333 test[8]=2.789474 class[8]=11.111111% ** CONFUSION MATRIX ** Number of classes: 2 10 1 2 14 PRECISION 0.8833333333 RECALL: 0.8920454545 train[9]=25.2026607333 test[9]=2.789474 class[9]=11.111111% ** CONFUSION MATRIX ** Number of classes: 2 10 1 2 14 PRECISION 0.8833333333 RECALL: 0.8920454545 train[10]=25.2026607333 test[10]=2.789474 class[10]=11.111111% ** CONFUSION MATRIX ** Number of classes: 2 10 1 2 14 PRECISION 0.8833333333 RECALL: 0.8920454545 train[11]=25.2026607333 test[11]=2.789474 class[11]=11.111111% ** CONFUSION MATRIX ** Number of classes: 2 10 1 2 14 PRECISION 0.8833333333 RECALL: 0.8920454545 train[12]=25.2026607333 test[12]=2.789474 class[12]=11.111111% ** CONFUSION MATRIX ** Number of classes: 2 10 1 2 14 PRECISION 0.8833333333 RECALL: 0.8920454545 train[13]=25.2026607333 test[13]=2.789474 class[13]=11.111111% ** CONFUSION MATRIX ** Number of classes: 2 10 1 2 14 PRECISION 0.8833333333 RECALL: 0.8920454545 train[14]=25.2026607333 test[14]=2.789474 class[14]=11.111111% ** CONFUSION MATRIX ** Number of classes: 2 10 1 2 14 PRECISION 0.8833333333 RECALL: 0.8920454545 train[15]=25.2026607333 test[15]=2.789474 class[15]=11.111111% ** CONFUSION MATRIX ** Number of classes: 2 10 1 2 14 PRECISION 0.8833333333 RECALL: 0.8920454545 train[16]=25.2026607333 test[16]=2.789474 class[16]=11.111111% ** CONFUSION MATRIX ** Number of classes: 2 10 1 2 14 PRECISION 0.8833333333 RECALL: 0.8920454545 train[17]=25.2026607333 test[17]=2.789474 class[17]=11.111111% ** CONFUSION MATRIX ** Number of classes: 2 10 1 2 14 PRECISION 0.8833333333 RECALL: 0.8920454545 train[18]=25.2026607333 test[18]=2.789474 class[18]=11.111111% ** CONFUSION MATRIX ** Number of classes: 2 10 1 2 14 PRECISION 0.8833333333 RECALL: 0.8920454545 train[19]=25.2026607333 test[19]=2.789474 class[19]=11.111111% ** CONFUSION MATRIX ** Number of classes: 2 10 1 2 14 PRECISION 0.8833333333 RECALL: 0.8920454545 train[20]=25.2026607333 test[20]=2.789474 class[20]=11.111111% ** CONFUSION MATRIX ** Number of classes: 2 10 1 2 14 PRECISION 0.8833333333 RECALL: 0.8920454545 train[21]=25.2026607333 test[21]=2.789474 class[21]=11.111111% ** CONFUSION MATRIX ** Number of classes: 2 10 1 2 14 PRECISION 0.8833333333 RECALL: 0.8920454545 train[22]=25.2026607333 test[22]=2.789474 class[22]=11.111111% ** CONFUSION MATRIX ** Number of classes: 2 10 1 2 14 PRECISION 0.8833333333 RECALL: 0.8920454545 train[23]=25.2026607333 test[23]=2.789474 class[23]=11.111111% ** CONFUSION MATRIX ** Number of classes: 2 10 1 2 14 PRECISION 0.8833333333 RECALL: 0.8920454545 train[24]=25.2026607333 test[24]=2.789474 class[24]=11.111111% ** CONFUSION MATRIX ** Number of classes: 2 10 1 2 14 PRECISION 0.8833333333 RECALL: 0.8920454545 train[25]=25.2026607333 test[25]=2.789474 class[25]=11.111111% ** CONFUSION MATRIX ** Number of classes: 2 10 1 2 14 PRECISION 0.8833333333 RECALL: 0.8920454545 train[26]=25.2026607333 test[26]=2.789474 class[26]=11.111111% ** CONFUSION MATRIX ** Number of classes: 2 10 1 2 14 PRECISION 0.8833333333 RECALL: 0.8920454545 train[27]=25.2026607333 test[27]=2.789474 class[27]=11.111111% ** CONFUSION MATRIX ** Number of classes: 2 10 1 2 14 PRECISION 0.8833333333 RECALL: 0.8920454545 train[28]=25.2026607333 test[28]=2.789474 class[28]=11.111111% ** CONFUSION MATRIX ** Number of classes: 2 10 1 2 14 PRECISION 0.8833333333 RECALL: 0.8920454545 train[29]=25.2026607333 test[29]=2.789474 class[29]=11.111111% ============================================================================= AVERAGES (TRAIN,TEST,CLASS): 25.202661 2.7894739 11.111111% AVERAGES (PRECISION,RECALL): 0.88333333 0.89204545
# Settings Tab: Advanced # Run Date: 04/09/2025 21:00:45 # Run Time: 00:22:28 (1348413 ms) @ Parameters: ============================================================ 1. Dataset File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/datasets/MLDATASETS-master/QFC_GUI_datasets/Classification/heart.data 2. Dataset Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.train 3. Dataset Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.test 4. Export Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.train 5. Export Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.test 6. Export Cpp File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.cpp 7. Features: 2 8. Feature Create Model: RBF 9. Feature Evaluate Model: RBF 10. Test Iters: 30 11. Random Seed: 1 12. Threads: 1 13. GE Chromosomes: 500 14. GE Max Generations: 200 15. GE Selection Rate: 0,10 16. GE Mutation Rate: 0,05 17. GE Length: 40 18. GE Local Search Generations: 20 19. GE Local Search Rate: 0,05 20. GE Local Search Method: Crossover 21. GE Method: Genetic 22. RBF Weights: 10 @ Results: =============================================================== Iteration: 1 Best Fitness: -37.3649 Best program: f1(x)=((-15.989)*x13*x12) f2(x)=02.94*x10+(-4.314)*x3+x9+x6 Iteration: 2 Best Fitness: -35.1017 Best program: f1(x)=(-815.483)*x13 f2(x)=(58.14*x3*abs(((-5.4)*x7-(x12+3.86*x3)))) Iteration: 3 Best Fitness: -32.3135 Best program: f1(x)=(x9+x11+75.1*x12) f2(x)=67.2*x3+(963.088/14.24)*x2+x7+39.996*x9+(0.67/236.963)*x5+(08.4/4.138)*x11 Iteration: 4 Best Fitness: -29.9344 Best program: f1(x)=abs((34.7*x3*(x8-((-854.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 5 Best Fitness: -29.6172 Best program: f1(x)=abs((34.7*x3*(x8-((-804.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 6 Best Fitness: -28.6181 Best program: f1(x)=(x9+x10+75.1*x12) f2(x)=(-67.2)*x3+(963.086/(-32.39))*x13+x1+((-42.28)/507.1)*x9+x9 Iteration: 7 Best Fitness: -27.4231 Best program: f1(x)=abs(((-34.5)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 8 Best Fitness: -27.4021 Best program: f1(x)=abs(((-34.5)*x3*(x4-((-854.77)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 9 Best Fitness: -26.4306 Best program: f1(x)=abs(((-44.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 10 Best Fitness: -26.4306 Best program: f1(x)=abs(((-44.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 11 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 12 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 13 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 14 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 15 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 16 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 17 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 18 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 19 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 20 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 21 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 22 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 23 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 24 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 25 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 26 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 27 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 28 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 29 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 30 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 31 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 32 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 33 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 34 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 35 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 36 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 37 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 38 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 39 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 40 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 41 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 42 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 43 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 44 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 45 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 46 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 47 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 48 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 49 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 50 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 51 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 52 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 53 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 54 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 55 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 56 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 57 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 58 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 59 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 60 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 61 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 62 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 63 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 64 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 65 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 66 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 67 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 68 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 69 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 70 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 71 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 72 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 73 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 74 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 75 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 76 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 77 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 78 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 79 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 80 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 81 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 82 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 83 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 84 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 85 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 86 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 87 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 88 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 89 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 90 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 91 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 92 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 93 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 94 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 95 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 96 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 97 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 98 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 99 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 100 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 101 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 102 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 103 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 104 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 105 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 106 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 107 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 108 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 109 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 110 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 111 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 112 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 113 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 114 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 115 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 116 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 117 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 118 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 119 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 120 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 121 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 122 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 123 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 124 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 125 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 126 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 127 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 128 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 129 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 130 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 131 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 132 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 133 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 134 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 135 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 136 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 137 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 138 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 139 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 140 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 141 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 142 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 143 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 144 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 145 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 146 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 147 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 148 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 149 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 150 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 151 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 152 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 153 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 154 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 155 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 156 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 157 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 158 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 159 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 160 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 161 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 162 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 163 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 164 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 165 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 166 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 167 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 168 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 169 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 170 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 171 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 172 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 173 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 174 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 175 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 176 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 177 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 178 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 179 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 180 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 181 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 182 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 183 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 184 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 185 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 186 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 187 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 188 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 189 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 190 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 191 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 192 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 193 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 194 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 195 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 196 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 197 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 198 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 199 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 200 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) ** CONFUSION MATRIX ** Number of classes: 2 8 1 4 14 PRECISION 0.8000000000 RECALL: 0.8333333333 train[0]=31.6013777467 test[0]=4.537580 class[0]=18.518519% ** CONFUSION MATRIX ** Number of classes: 2 8 1 4 14 PRECISION 0.8000000000 RECALL: 0.8333333333 train[1]=31.7807179355 test[1]=4.688846 class[1]=18.518519% ** CONFUSION MATRIX ** Number of classes: 2 9 1 3 14 PRECISION 0.8416666667 RECALL: 0.8617647059 train[2]=31.1155729828 test[2]=4.627102 class[2]=14.814815% ** CONFUSION MATRIX ** Number of classes: 2 7 3 5 12 PRECISION 0.6916666667 RECALL: 0.7029411765 train[3]=34.6021031792 test[3]=6.460408 class[3]=29.629630% ** CONFUSION MATRIX ** Number of classes: 2 9 2 3 13 PRECISION 0.8083333333 RECALL: 0.8153409091 train[4]=27.8744638882 test[4]=3.767803 class[4]=18.518519% ** CONFUSION MATRIX ** Number of classes: 2 8 2 4 13 PRECISION 0.7666666667 RECALL: 0.7823529412 train[5]=26.9855421589 test[5]=3.738533 class[5]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 8 2 4 13 PRECISION 0.7666666667 RECALL: 0.7823529412 train[6]=30.2383367188 test[6]=4.572193 class[6]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 8 2 4 13 PRECISION 0.7666666667 RECALL: 0.7823529412 train[7]=31.4997492814 test[7]=4.856934 class[7]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 7 2 5 13 PRECISION 0.7250000000 RECALL: 0.7500000000 train[8]=31.2608434500 test[8]=4.950515 class[8]=25.925926% ** CONFUSION MATRIX ** Number of classes: 2 8 2 4 13 PRECISION 0.7666666667 RECALL: 0.7823529412 train[9]=26.6227227340 test[9]=3.801555 class[9]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 9 1 3 14 PRECISION 0.8416666667 RECALL: 0.8617647059 train[10]=31.6214569507 test[10]=4.574447 class[10]=14.814815% ** CONFUSION MATRIX ** Number of classes: 2 6 2 6 13 PRECISION 0.6833333333 RECALL: 0.7171052632 train[11]=56.1461887442 test[11]=8.852024 class[11]=29.629630% ** CONFUSION MATRIX ** Number of classes: 2 8 2 4 13 PRECISION 0.7666666667 RECALL: 0.7823529412 train[12]=31.7412718907 test[12]=4.861070 class[12]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 8 2 4 13 PRECISION 0.7666666667 RECALL: 0.7823529412 train[13]=26.6556084794 test[13]=3.872114 class[13]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 7 1 5 14 PRECISION 0.7583333333 RECALL: 0.8059210526 train[14]=33.5617851851 test[14]=4.879140 class[14]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 8 2 4 13 PRECISION 0.7666666667 RECALL: 0.7823529412 train[15]=26.7556611332 test[15]=3.912199 class[15]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 8 2 4 13 PRECISION 0.7666666667 RECALL: 0.7823529412 train[16]=26.6556040526 test[16]=3.872439 class[16]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 9 1 3 14 PRECISION 0.8416666667 RECALL: 0.8617647059 train[17]=31.1155729828 test[17]=4.627102 class[17]=14.814815% ** CONFUSION MATRIX ** Number of classes: 2 9 1 3 14 PRECISION 0.8416666667 RECALL: 0.8617647059 train[18]=28.6434228377 test[18]=3.814055 class[18]=14.814815% ** CONFUSION MATRIX ** Number of classes: 2 9 2 3 13 PRECISION 0.8083333333 RECALL: 0.8153409091 train[19]=27.7929607004 test[19]=4.342120 class[19]=18.518519% ** CONFUSION MATRIX ** Number of classes: 2 8 2 4 13 PRECISION 0.7666666667 RECALL: 0.7823529412 train[20]=26.6617713422 test[20]=3.876597 class[20]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 8 2 4 13 PRECISION 0.7666666667 RECALL: 0.7823529412 train[21]=32.0059615919 test[21]=5.000233 class[21]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 7 2 5 13 PRECISION 0.7250000000 RECALL: 0.7500000000 train[22]=30.0584182638 test[22]=4.374390 class[22]=25.925926% ** CONFUSION MATRIX ** Number of classes: 2 2 0 10 15 PRECISION 0.5833333333 RECALL: 0.8000000000 train[23]=8226.5392034721 test[23]=979.474163 class[23]=37.037037% ** CONFUSION MATRIX ** Number of classes: 2 7 2 5 13 PRECISION 0.7250000000 RECALL: 0.7500000000 train[24]=31.2608434501 test[24]=4.950516 class[24]=25.925926% ** CONFUSION MATRIX ** Number of classes: 2 9 1 3 14 PRECISION 0.8416666667 RECALL: 0.8617647059 train[25]=31.1155729828 test[25]=4.627102 class[25]=14.814815% ** CONFUSION MATRIX ** Number of classes: 2 9 3 3 12 PRECISION 0.7750000000 RECALL: 0.7750000000 train[26]=33.8657187939 test[26]=4.991984 class[26]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 9 2 3 13 PRECISION 0.8083333333 RECALL: 0.8153409091 train[27]=31.7049891897 test[27]=4.644567 class[27]=18.518519% ** CONFUSION MATRIX ** Number of classes: 2 9 2 3 13 PRECISION 0.8083333333 RECALL: 0.8153409091 train[28]=27.7706507638 test[28]=3.807696 class[28]=18.518519% ** CONFUSION MATRIX ** Number of classes: 2 8 2 4 13 PRECISION 0.7666666667 RECALL: 0.7823529412 train[29]=31.4646080097 test[29]=4.917602 class[29]=22.222222% ============================================================================= AVERAGES (TRAIN, TEST, CLASS): 304.22396 37.142434 21.604938% AVERAGES (PRECISION, RECALL): 0.77138889 0.79645679
# Settings Tab: Advanced # Run Date: 04/09/2025 21:52:08 # Run Time: 00:27:51 (1671624 ms) @ Parameters: ============================================================ 1. Dataset File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/datasets/MLDATASETS-master/QFC_GUI_datasets/Classification/heart.data 2. Dataset Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.train 3. Dataset Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.test 4. Export Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.train 5. Export Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.test 6. Export Cpp File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.cpp 7. Features: 2 8. Feature Create Model: RBF 9. Feature Evaluate Model: Neural 10. Test Iters: 30 11. Random Seed: 1 12. Threads: 1 13. GE Chromosomes: 500 14. GE Max Generations: 200 15. GE Selection Rate: 0,10 16. GE Mutation Rate: 0,05 17. GE Length: 40 18. GE Local Search Generations: 20 19. GE Local Search Rate: 0,05 20. GE Local Search Method: Crossover 21. GE Method: Genetic 22. RBF Weights: 10 23. Neural Weights: 10 24. Neural Training Method: Genetic 25. BFGS Iteration: 2001 26. GN Chromosomes: 500 27. GN Max Generations: 200 28. GN Selection Rate: 0,10 29. GN Mutation Rate: 0,05 30. GN Local Search Rate: 0,00 31. GN Local Search Method: BFGS @ Results: =============================================================== Iteration: 1 Best Fitness: -37.3649 Best program: f1(x)=((-15.989)*x13*x12) f2(x)=02.94*x10+(-4.314)*x3+x9+x6 Iteration: 2 Best Fitness: -35.1017 Best program: f1(x)=(-815.483)*x13 f2(x)=(58.14*x3*abs(((-5.4)*x7-(x12+3.86*x3)))) Iteration: 3 Best Fitness: -32.3135 Best program: f1(x)=(x9+x11+75.1*x12) f2(x)=67.2*x3+(963.088/14.24)*x2+x7+39.996*x9+(0.67/236.963)*x5+(08.4/4.138)*x11 Iteration: 4 Best Fitness: -29.9344 Best program: f1(x)=abs((34.7*x3*(x8-((-854.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 5 Best Fitness: -29.6172 Best program: f1(x)=abs((34.7*x3*(x8-((-804.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 6 Best Fitness: -28.6181 Best program: f1(x)=(x9+x10+75.1*x12) f2(x)=(-67.2)*x3+(963.086/(-32.39))*x13+x1+((-42.28)/507.1)*x9+x9 Iteration: 7 Best Fitness: -27.4231 Best program: f1(x)=abs(((-34.5)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 8 Best Fitness: -27.4021 Best program: f1(x)=abs(((-34.5)*x3*(x4-((-854.77)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 9 Best Fitness: -26.4306 Best program: f1(x)=abs(((-44.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 10 Best Fitness: -26.4306 Best program: f1(x)=abs(((-44.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 11 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 12 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 13 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 14 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 15 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 16 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 17 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 18 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 19 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 20 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 21 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 22 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 23 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 24 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 25 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 26 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 27 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 28 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 29 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 30 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 31 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 32 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 33 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 34 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 35 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 36 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 37 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 38 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 39 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 40 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 41 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 42 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 43 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 44 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 45 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 46 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 47 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 48 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 49 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 50 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 51 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 52 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 53 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 54 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 55 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 56 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 57 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 58 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 59 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 60 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 61 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 62 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 63 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 64 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 65 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 66 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 67 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 68 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 69 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 70 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 71 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 72 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 73 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 74 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 75 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 76 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 77 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 78 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 79 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 80 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 81 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 82 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 83 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 84 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 85 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 86 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 87 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 88 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 89 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 90 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 91 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 92 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 93 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 94 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 95 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 96 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 97 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 98 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 99 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 100 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 101 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 102 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 103 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 104 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 105 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 106 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 107 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 108 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 109 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 110 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 111 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 112 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 113 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 114 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 115 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 116 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 117 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 118 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 119 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 120 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 121 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 122 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 123 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 124 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 125 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 126 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 127 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 128 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 129 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 130 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 131 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 132 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 133 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 134 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 135 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 136 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 137 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 138 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 139 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 140 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 141 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 142 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 143 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 144 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 145 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 146 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 147 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 148 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 149 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 150 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 151 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 152 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 153 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 154 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 155 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 156 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 157 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 158 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 159 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 160 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 161 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 162 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 163 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 164 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 165 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 166 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 167 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 168 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 169 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 170 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 171 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 172 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 173 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 174 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 175 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 176 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 177 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 178 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 179 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 180 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 181 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 182 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 183 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 184 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 185 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 186 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 187 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 188 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 189 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 190 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 191 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 192 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 193 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 194 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 195 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 196 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 197 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 198 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 199 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) Iteration: 200 Best Fitness: -26.3991 Best program: f1(x)=abs(((-43.2)*x3*(x4-((-954.17)*x12)))) f2(x)=(x11*((-185.789)*x13)) ** CONFUSION MATRIX ** Number of classes: 2 9 3 3 12 PRECISION 0.7750000000 RECALL: 0.7750000000 train[0]=40.7218294234 test[0]=4.449998 class[0]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 9 3 3 12 PRECISION 0.7750000000 RECALL: 0.7750000000 train[1]=40.7218294234 test[1]=4.449998 class[1]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 9 3 3 12 PRECISION 0.7750000000 RECALL: 0.7750000000 train[2]=40.7218294234 test[2]=4.449998 class[2]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 9 3 3 12 PRECISION 0.7750000000 RECALL: 0.7750000000 train[3]=40.7218294234 test[3]=4.449998 class[3]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 9 3 3 12 PRECISION 0.7750000000 RECALL: 0.7750000000 train[4]=40.7218294234 test[4]=4.449998 class[4]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 9 3 3 12 PRECISION 0.7750000000 RECALL: 0.7750000000 train[5]=40.7218294234 test[5]=4.449998 class[5]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 9 3 3 12 PRECISION 0.7750000000 RECALL: 0.7750000000 train[6]=40.7218294234 test[6]=4.449998 class[6]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 9 3 3 12 PRECISION 0.7750000000 RECALL: 0.7750000000 train[7]=40.7218294234 test[7]=4.449998 class[7]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 9 3 3 12 PRECISION 0.7750000000 RECALL: 0.7750000000 train[8]=40.7218294234 test[8]=4.449998 class[8]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 9 3 3 12 PRECISION 0.7750000000 RECALL: 0.7750000000 train[9]=40.7218294234 test[9]=4.449998 class[9]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 9 3 3 12 PRECISION 0.7750000000 RECALL: 0.7750000000 train[10]=40.7218294234 test[10]=4.449998 class[10]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 9 3 3 12 PRECISION 0.7750000000 RECALL: 0.7750000000 train[11]=40.7218294234 test[11]=4.449998 class[11]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 9 3 3 12 PRECISION 0.7750000000 RECALL: 0.7750000000 train[12]=40.7218294234 test[12]=4.449998 class[12]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 9 3 3 12 PRECISION 0.7750000000 RECALL: 0.7750000000 train[13]=40.7218294234 test[13]=4.449998 class[13]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 9 3 3 12 PRECISION 0.7750000000 RECALL: 0.7750000000 train[14]=40.7218294234 test[14]=4.449998 class[14]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 9 3 3 12 PRECISION 0.7750000000 RECALL: 0.7750000000 train[15]=40.7218294234 test[15]=4.449998 class[15]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 9 3 3 12 PRECISION 0.7750000000 RECALL: 0.7750000000 train[16]=40.7218294234 test[16]=4.449998 class[16]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 9 3 3 12 PRECISION 0.7750000000 RECALL: 0.7750000000 train[17]=40.7218294234 test[17]=4.449998 class[17]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 9 3 3 12 PRECISION 0.7750000000 RECALL: 0.7750000000 train[18]=40.7218294234 test[18]=4.449998 class[18]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 9 3 3 12 PRECISION 0.7750000000 RECALL: 0.7750000000 train[19]=40.7218294234 test[19]=4.449998 class[19]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 9 3 3 12 PRECISION 0.7750000000 RECALL: 0.7750000000 train[20]=40.7218294234 test[20]=4.449998 class[20]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 9 3 3 12 PRECISION 0.7750000000 RECALL: 0.7750000000 train[21]=40.7218294234 test[21]=4.449998 class[21]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 9 3 3 12 PRECISION 0.7750000000 RECALL: 0.7750000000 train[22]=40.7218294234 test[22]=4.449998 class[22]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 9 3 3 12 PRECISION 0.7750000000 RECALL: 0.7750000000 train[23]=40.7218294234 test[23]=4.449998 class[23]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 9 3 3 12 PRECISION 0.7750000000 RECALL: 0.7750000000 train[24]=40.7218294234 test[24]=4.449998 class[24]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 9 3 3 12 PRECISION 0.7750000000 RECALL: 0.7750000000 train[25]=40.7218294234 test[25]=4.449998 class[25]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 9 3 3 12 PRECISION 0.7750000000 RECALL: 0.7750000000 train[26]=40.7218294234 test[26]=4.449998 class[26]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 9 3 3 12 PRECISION 0.7750000000 RECALL: 0.7750000000 train[27]=40.7218294234 test[27]=4.449998 class[27]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 9 3 3 12 PRECISION 0.7750000000 RECALL: 0.7750000000 train[28]=40.7218294234 test[28]=4.449998 class[28]=22.222222% ** CONFUSION MATRIX ** Number of classes: 2 9 3 3 12 PRECISION 0.7750000000 RECALL: 0.7750000000 train[29]=40.7218294234 test[29]=4.449998 class[29]=22.222222% ============================================================================= AVERAGES (TRAIN, TEST, CLASS): 40.721829 4.4499977 22.222222% AVERAGES (PRECISION, RECALL): 0.775 0.775
# Settings Tab: Advanced # Run Date: 05/09/2025 17:50:03 # Run Time: 00:00:00 (118 ms) @ Parameters: ============================================================ 1. Dataset File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/datasets/MLDATASETS-master/QFC_GUI_datasets/Classification/appendicitis.data 2. Dataset Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.train 3. Dataset Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.test 4. Export Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.train 5. Export Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.test 6. Export Cpp File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.cpp 7. Features: 1 8. Feature Create Model: Copy 9. Feature Evaluate Model: RBF 10. Test Iters: 30 11. Random Seed: 1 12. Threads: 1 13. RBF Weights: 10 @ Results: =============================================================== ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[0]=7.6435032626 test[0]=1.067907 class[0]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[1]=7.2978886637 test[1]=1.101884 class[1]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[2]=7.5606690790 test[2]=1.075813 class[2]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[3]=7.3470077993 test[3]=1.151776 class[3]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[4]=7.2356293760 test[4]=1.169196 class[4]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[5]=7.3440289744 test[5]=1.129633 class[5]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[6]=7.3037632116 test[6]=1.094688 class[6]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[7]=7.4905133734 test[7]=1.082342 class[7]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[8]=7.2613425292 test[8]=1.153153 class[8]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[9]=7.7804074673 test[9]=1.087106 class[9]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[10]=7.4754377440 test[10]=1.096616 class[10]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[11]=7.4265966509 test[11]=1.099143 class[11]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[12]=7.2741553647 test[12]=1.151313 class[12]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[13]=7.5539833110 test[13]=1.043004 class[13]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[14]=7.4802107952 test[14]=1.071998 class[14]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[15]=7.3938844919 test[15]=1.094030 class[15]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[16]=7.4952738368 test[16]=1.122175 class[16]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[17]=7.4203512159 test[17]=1.059582 class[17]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[18]=7.3043872158 test[18]=1.152102 class[18]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[19]=7.4767591631 test[19]=1.139307 class[19]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[20]=7.4934121795 test[20]=1.080130 class[20]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[21]=7.3509248226 test[21]=1.126832 class[21]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[22]=7.4238809196 test[22]=1.092636 class[22]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[23]=7.2610196830 test[23]=1.142045 class[23]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[24]=7.3315895596 test[24]=1.111088 class[24]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[25]=7.6426654532 test[25]=1.053209 class[25]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[26]=7.8215572811 test[26]=1.039033 class[26]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[27]=7.3612732259 test[27]=1.140831 class[27]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[28]=7.3414852783 test[28]=1.097940 class[28]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[29]=7.2580934302 test[29]=1.142715 class[29]=10.000000% ============================================================================= AVERAGES (TRAIN,TEST,CLASS): 7.4283898 1.105641 10% AVERAGES (PRECISION,RECALL): 0.75 0.94444444
# Settings Tab: Advanced # Run Date: 05/09/2025 18:06:49 # Run Time: 00:05:19 (319089 ms) @ Parameters: ============================================================ 1. Dataset File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/datasets/MLDATASETS-master/QFC_GUI_datasets/Classification/appendicitis.data 2. Dataset Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.train 3. Dataset Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.test 4. Export Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.train 5. Export Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.test 6. Export Cpp File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.cpp 7. Features: 1 8. Feature Create Model: Copy 9. Feature Evaluate Model: Neural 10. Test Iters: 30 11. Random Seed: 1 12. Threads: 1 13. Neural Weights: 10 14. Neural Training Method: Genetic 15. BFGS Iteration: 2001 16. GN Chromosomes: 500 17. GN Max Generations: 200 18. GN Selection Rate: 0,10 19. GN Mutation Rate: 0,05 20. GN Local Search Rate: 0,00 21. GN Local Search Method: BFGS @ Results: =============================================================== ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[0]=7.4362988898 test[0]=0.956594 class[0]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[1]=7.4362988898 test[1]=0.956594 class[1]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[2]=7.4362988898 test[2]=0.956594 class[2]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[3]=7.4362988898 test[3]=0.956594 class[3]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[4]=7.4362988898 test[4]=0.956594 class[4]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[5]=7.4362988898 test[5]=0.956594 class[5]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[6]=7.4362988898 test[6]=0.956594 class[6]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[7]=7.4362988898 test[7]=0.956594 class[7]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[8]=7.4362988898 test[8]=0.956594 class[8]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[9]=7.4362988898 test[9]=0.956594 class[9]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[10]=7.4362988898 test[10]=0.956594 class[10]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[11]=7.4362988898 test[11]=0.956594 class[11]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[12]=7.4362988898 test[12]=0.956594 class[12]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[13]=7.4362988898 test[13]=0.956594 class[13]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[14]=7.4362988898 test[14]=0.956594 class[14]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[15]=7.4362988898 test[15]=0.956594 class[15]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[16]=7.4362988898 test[16]=0.956594 class[16]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[17]=7.4362988898 test[17]=0.956594 class[17]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[18]=7.4362988898 test[18]=0.956594 class[18]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[19]=7.4362988898 test[19]=0.956594 class[19]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[20]=7.4362988898 test[20]=0.956594 class[20]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[21]=7.4362988898 test[21]=0.956594 class[21]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[22]=7.4362988898 test[22]=0.956594 class[22]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[23]=7.4362988898 test[23]=0.956594 class[23]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[24]=7.4362988898 test[24]=0.956594 class[24]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[25]=7.4362988898 test[25]=0.956594 class[25]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[26]=7.4362988898 test[26]=0.956594 class[26]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[27]=7.4362988898 test[27]=0.956594 class[27]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[28]=7.4362988898 test[28]=0.956594 class[28]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[29]=7.4362988898 test[29]=0.956594 class[29]=10.000000% ============================================================================= AVERAGES (TRAIN, TEST, CLASS): 7.4362989 0.95659366 10% AVERAGES (PRECISION, RECALL): 0.75 0.94444444
# Settings Tab: Advanced # Run Date: 05/09/2025 18:28:32 # Run Time: 00:03:00 (180982 ms) @ Parameters: ============================================================ 1. Dataset File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/datasets/MLDATASETS-master/QFC_GUI_datasets/Classification/appendicitis.data 2. Dataset Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.train 3. Dataset Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.test 4. Export Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.train 5. Export Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.test 6. Export Cpp File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.cpp 7. Features: 2 8. Feature Create Model: RBF 9. Feature Evaluate Model: RBF 10. Test Iters: 30 11. Random Seed: 1 12. Threads: 1 13. GE Chromosomes: 500 14. GE Max Generations: 200 15. GE Selection Rate: 0,10 16. GE Mutation Rate: 0,05 17. GE Length: 40 18. GE Local Search Generations: 20 19. GE Local Search Rate: 0,05 20. GE Local Search Method: Crossover 21. GE Method: Genetic 22. RBF Weights: 10 @ Results: =============================================================== Iteration: 1 Best Fitness: -7.02193 Best program: f1(x)=((-7.8)*x6/(-825.79)*x5) f2(x)=abs(x5+x7) Iteration: 2 Best Fitness: -7.02193 Best program: f1(x)=((-7.8)*x6/(-825.79)*x5) f2(x)=abs(x5+x7) Iteration: 3 Best Fitness: -6.67723 Best program: f1(x)=sin((x1+(9.7*x7/(5.109*x3-(217.16*x6))))) f2(x)=x6+x4 Iteration: 4 Best Fitness: -6.57889 Best program: f1(x)=((-03.79)/(-71.967))*x7 f2(x)=abs(x4+x7) Iteration: 5 Best Fitness: -6.56358 Best program: f1(x)=(03.72/(-73.922))*x1 f2(x)=abs(x4+x7) Iteration: 6 Best Fitness: -6.55057 Best program: f1(x)=((-03.79)/(-71.967))*x1 f2(x)=abs(x4+x7) Iteration: 7 Best Fitness: -6.46273 Best program: f1(x)=(05.79/(-71.967))*x4 f2(x)=abs(x4+x7) Iteration: 8 Best Fitness: -6.46273 Best program: f1(x)=(05.79/(-71.967))*x4 f2(x)=abs(x4+x7) Iteration: 9 Best Fitness: -6.45809 Best program: f1(x)=(05.42/(-63.922))*x4 f2(x)=abs(x4+x7) Iteration: 10 Best Fitness: -6.45797 Best program: f1(x)=(05.49/(-61.967))*x4 f2(x)=abs(x4+x7) Iteration: 11 Best Fitness: -6.45759 Best program: f1(x)=(05.52/(-63.922))*x4 f2(x)=abs(x4+x7) Iteration: 12 Best Fitness: -6.45757 Best program: f1(x)=(05.47/63.122)*x4 f2(x)=abs(x4+x7) Iteration: 13 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.977))*x4 f2(x)=abs(x4+x7) Iteration: 14 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.925))*x4 f2(x)=abs(x4+x7) Iteration: 15 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.925))*x4 f2(x)=abs(x4+x7) Iteration: 16 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.925))*x4 f2(x)=abs(x4+x7) Iteration: 17 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 18 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 19 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 20 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 21 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 22 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 23 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 24 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 25 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 26 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 27 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 28 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 29 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 30 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 31 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 32 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 33 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 34 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 35 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 36 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 37 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 38 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 39 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 40 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 41 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 42 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 43 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 44 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 45 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 46 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 47 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 48 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 49 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 50 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 51 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 52 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 53 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 54 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 55 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 56 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 57 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 58 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 59 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 60 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 61 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 62 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 63 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 64 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 65 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 66 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 67 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 68 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 69 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 70 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 71 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 72 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 73 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 74 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 75 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 76 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 77 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 78 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 79 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 80 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 81 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 82 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 83 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 84 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 85 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 86 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 87 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 88 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 89 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 90 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 91 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 92 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 93 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 94 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 95 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 96 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 97 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 98 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 99 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 100 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 101 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 102 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 103 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 104 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 105 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 106 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 107 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 108 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 109 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 110 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 111 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 112 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 113 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 114 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 115 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 116 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 117 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 118 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 119 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 120 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 121 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 122 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 123 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 124 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 125 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 126 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 127 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 128 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 129 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 130 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 131 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 132 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 133 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 134 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 135 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 136 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 137 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 138 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 139 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 140 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 141 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 142 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 143 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 144 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 145 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 146 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 147 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 148 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 149 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 150 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 151 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 152 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 153 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 154 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 155 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 156 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 157 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 158 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 159 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 160 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 161 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 162 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 163 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 164 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 165 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 166 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 167 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 168 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 169 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 170 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 171 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 172 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 173 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 174 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 175 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 176 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 177 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 178 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 179 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 180 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 181 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 182 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 183 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 184 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 185 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 186 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 187 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 188 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 189 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 190 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 191 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 192 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 193 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 194 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 195 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 196 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 197 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 198 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 199 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 200 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[0]=7.4557397234 test[0]=1.310010 class[0]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[1]=7.5685683315 test[1]=1.206724 class[1]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[2]=7.6864194919 test[2]=1.164587 class[2]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[3]=7.7782847755 test[3]=1.180401 class[3]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[4]=7.1404469320 test[4]=1.346615 class[4]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[5]=7.6864194919 test[5]=1.164587 class[5]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[6]=7.6493610995 test[6]=1.202188 class[6]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 2 0 0 PRECISION 0.5000000000 RECALL: 0.4000000000 train[7]=7.5701982469 test[7]=1.533762 class[7]=20.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[8]=7.6864194919 test[8]=1.164587 class[8]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[9]=7.7782847755 test[9]=1.180401 class[9]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[10]=7.5640229916 test[10]=1.194585 class[10]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[11]=7.2586169997 test[11]=1.356684 class[11]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[12]=7.5405999610 test[12]=1.207395 class[12]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 2 0 0 PRECISION 0.5000000000 RECALL: 0.4000000000 train[13]=7.2103596936 test[13]=1.589855 class[13]=20.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[14]=7.3969815561 test[14]=1.332076 class[14]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[15]=7.6711770258 test[15]=1.216177 class[15]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[16]=7.6444616714 test[16]=1.178234 class[16]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[17]=7.6864194919 test[17]=1.164587 class[17]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[18]=7.6175190345 test[18]=1.202145 class[18]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[19]=7.5728060914 test[19]=1.196834 class[19]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[20]=7.6864194919 test[20]=1.164587 class[20]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[21]=7.8570406781 test[21]=1.284070 class[21]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[22]=7.5701853950 test[22]=1.214000 class[22]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 2 0 0 PRECISION 0.5000000000 RECALL: 0.4000000000 train[23]=7.3674594000 test[23]=1.791155 class[23]=20.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[24]=7.4321729640 test[24]=1.251578 class[24]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 2 0 0 PRECISION 0.5000000000 RECALL: 0.4000000000 train[25]=6.6031650928 test[25]=2.103642 class[25]=20.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[26]=7.5889752871 test[26]=1.274440 class[26]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[27]=7.6711770258 test[27]=1.216177 class[27]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 2 0 0 PRECISION 0.5000000000 RECALL: 0.4000000000 train[28]=6.4135791310 test[28]=2.979392 class[28]=20.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[29]=7.3290294889 test[29]=1.415602 class[29]=10.000000% ============================================================================= AVERAGES (TRAIN, TEST, CLASS): 7.4894104 1.3595692 11.666667% AVERAGES (PRECISION, RECALL): 0.70833333 0.8537037
# Settings Tab: Advanced # Run Date: 05/09/2025 19:06:12 # Run Time: 00:06:28 (388432 ms) @ Parameters: ============================================================ 1. Dataset File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/datasets/MLDATASETS-master/QFC_GUI_datasets/Classification/appendicitis.data 2. Dataset Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.train 3. Dataset Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.test 4. Export Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.train 5. Export Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.test 6. Export Cpp File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.cpp 7. Features: 2 8. Feature Create Model: RBF 9. Feature Evaluate Model: Neural 10. Test Iters: 30 11. Random Seed: 1 12. Threads: 1 13. GE Chromosomes: 500 14. GE Max Generations: 200 15. GE Selection Rate: 0,10 16. GE Mutation Rate: 0,05 17. GE Length: 40 18. GE Local Search Generations: 20 19. GE Local Search Rate: 0,05 20. GE Local Search Method: Crossover 21. GE Method: Genetic 22. RBF Weights: 10 23. Neural Weights: 10 24. Neural Training Method: Genetic 25. BFGS Iteration: 2001 26. GN Chromosomes: 500 27. GN Max Generations: 200 28. GN Selection Rate: 0,10 29. GN Mutation Rate: 0,05 30. GN Local Search Rate: 0,00 31. GN Local Search Method: BFGS @ Results: =============================================================== Iteration: 1 Best Fitness: -7.02193 Best program: f1(x)=((-7.8)*x6/(-825.79)*x5) f2(x)=abs(x5+x7) Iteration: 2 Best Fitness: -7.02193 Best program: f1(x)=((-7.8)*x6/(-825.79)*x5) f2(x)=abs(x5+x7) Iteration: 3 Best Fitness: -6.67723 Best program: f1(x)=sin((x1+(9.7*x7/(5.109*x3-(217.16*x6))))) f2(x)=x6+x4 Iteration: 4 Best Fitness: -6.57889 Best program: f1(x)=((-03.79)/(-71.967))*x7 f2(x)=abs(x4+x7) Iteration: 5 Best Fitness: -6.56358 Best program: f1(x)=(03.72/(-73.922))*x1 f2(x)=abs(x4+x7) Iteration: 6 Best Fitness: -6.55057 Best program: f1(x)=((-03.79)/(-71.967))*x1 f2(x)=abs(x4+x7) Iteration: 7 Best Fitness: -6.46273 Best program: f1(x)=(05.79/(-71.967))*x4 f2(x)=abs(x4+x7) Iteration: 8 Best Fitness: -6.46273 Best program: f1(x)=(05.79/(-71.967))*x4 f2(x)=abs(x4+x7) Iteration: 9 Best Fitness: -6.45809 Best program: f1(x)=(05.42/(-63.922))*x4 f2(x)=abs(x4+x7) Iteration: 10 Best Fitness: -6.45797 Best program: f1(x)=(05.49/(-61.967))*x4 f2(x)=abs(x4+x7) Iteration: 11 Best Fitness: -6.45759 Best program: f1(x)=(05.52/(-63.922))*x4 f2(x)=abs(x4+x7) Iteration: 12 Best Fitness: -6.45757 Best program: f1(x)=(05.47/63.122)*x4 f2(x)=abs(x4+x7) Iteration: 13 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.977))*x4 f2(x)=abs(x4+x7) Iteration: 14 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.925))*x4 f2(x)=abs(x4+x7) Iteration: 15 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.925))*x4 f2(x)=abs(x4+x7) Iteration: 16 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.925))*x4 f2(x)=abs(x4+x7) Iteration: 17 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 18 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 19 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 20 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 21 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 22 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 23 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 24 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 25 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 26 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 27 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 28 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 29 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 30 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 31 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 32 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 33 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 34 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 35 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 36 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 37 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 38 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 39 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 40 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 41 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 42 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 43 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 44 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 45 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 46 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 47 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 48 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 49 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 50 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 51 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 52 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 53 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 54 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 55 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 56 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 57 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 58 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 59 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 60 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 61 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 62 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 63 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 64 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 65 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 66 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 67 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 68 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 69 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 70 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 71 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 72 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 73 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 74 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 75 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 76 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 77 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 78 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 79 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 80 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 81 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 82 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 83 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 84 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 85 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 86 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 87 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 88 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 89 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 90 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 91 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 92 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 93 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 94 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 95 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 96 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 97 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 98 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 99 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 100 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 101 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 102 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 103 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 104 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 105 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 106 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 107 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 108 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 109 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 110 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 111 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 112 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 113 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 114 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 115 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 116 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 117 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 118 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 119 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 120 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 121 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 122 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 123 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 124 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 125 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 126 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 127 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 128 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 129 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 130 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 131 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 132 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 133 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 134 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 135 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 136 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 137 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 138 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 139 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 140 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 141 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 142 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 143 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 144 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 145 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 146 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 147 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 148 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 149 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 150 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 151 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 152 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 153 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 154 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 155 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 156 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 157 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 158 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 159 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 160 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 161 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 162 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 163 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 164 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 165 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 166 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 167 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 168 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 169 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 170 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 171 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 172 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 173 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 174 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 175 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 176 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 177 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 178 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 179 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 180 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 181 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 182 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 183 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 184 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 185 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 186 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 187 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 188 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 189 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 190 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 191 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 192 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 193 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 194 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 195 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 196 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 197 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 198 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 199 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) Iteration: 200 Best Fitness: -6.45756 Best program: f1(x)=(05.55/(-63.927))*x4 f2(x)=abs(x4+x7) ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[0]=8.2570668000 test[0]=0.945776 class[0]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[1]=8.2570668000 test[1]=0.945776 class[1]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[2]=8.2570668000 test[2]=0.945776 class[2]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[3]=8.2570668000 test[3]=0.945776 class[3]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[4]=8.2570668000 test[4]=0.945776 class[4]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[5]=8.2570668000 test[5]=0.945776 class[5]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[6]=8.2570668000 test[6]=0.945776 class[6]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[7]=8.2570668000 test[7]=0.945776 class[7]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[8]=8.2570668000 test[8]=0.945776 class[8]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[9]=8.2570668000 test[9]=0.945776 class[9]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[10]=8.2570668000 test[10]=0.945776 class[10]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[11]=8.2570668000 test[11]=0.945776 class[11]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[12]=8.2570668000 test[12]=0.945776 class[12]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[13]=8.2570668000 test[13]=0.945776 class[13]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[14]=8.2570668000 test[14]=0.945776 class[14]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[15]=8.2570668000 test[15]=0.945776 class[15]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[16]=8.2570668000 test[16]=0.945776 class[16]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[17]=8.2570668000 test[17]=0.945776 class[17]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[18]=8.2570668000 test[18]=0.945776 class[18]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[19]=8.2570668000 test[19]=0.945776 class[19]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[20]=8.2570668000 test[20]=0.945776 class[20]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[21]=8.2570668000 test[21]=0.945776 class[21]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[22]=8.2570668000 test[22]=0.945776 class[22]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[23]=8.2570668000 test[23]=0.945776 class[23]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[24]=8.2570668000 test[24]=0.945776 class[24]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[25]=8.2570668000 test[25]=0.945776 class[25]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[26]=8.2570668000 test[26]=0.945776 class[26]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[27]=8.2570668000 test[27]=0.945776 class[27]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[28]=8.2570668000 test[28]=0.945776 class[28]=10.000000% ** CONFUSION MATRIX ** Number of classes: 2 8 1 0 1 PRECISION 0.7500000000 RECALL: 0.9444444444 train[29]=8.2570668000 test[29]=0.945776 class[29]=10.000000% ============================================================================= AVERAGES (TRAIN, TEST, CLASS): 8.2570668 0.94577625 10% AVERAGES (PRECISION, RECALL): 0.75 0.94444444
# Settings Tab: Advanced # Run Date: 06/09/2025 15:16:22 # Run Time: 00:00:10 (10926 ms) @ Parameters: ============================================================ 1. Dataset File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFC_GUI/build/Desktop_Qt_5_15_2_MinGW_32_bit-Debug/debug/QFC_GUI_datasets/Regression/abalone.data 2. Dataset Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFC_GUI/build/Desktop_Qt_5_15_2_MinGW_32_bit-Debug/debug/exports/abalone.train 3. Dataset Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFC_GUI/build/Desktop_Qt_5_15_2_MinGW_32_bit-Debug/debug/exports/abalone.test 4. Export Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFC_GUI/build/Desktop_Qt_5_15_2_MinGW_32_bit-Debug/debug/exports/export.train 5. Export Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFC_GUI/build/Desktop_Qt_5_15_2_MinGW_32_bit-Debug/debug/exports/export.test 6. Export Cpp File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFC_GUI/build/Desktop_Qt_5_15_2_MinGW_32_bit-Debug/debug/exports/export.cpp 7. Features: 1 8. Feature Create Model: Copy 9. Feature Evaluate Model: RBF 10. Test Iters: 30 11. Random Seed: 1 12. Threads: 1 13. RBF Weights: 10 @ Results: =============================================================== train[0]=26212.0525070282 test[0]=3579.543637 train[1]=26528.4827632451 test[1]=3619.370022 train[2]=26575.8278232673 test[2]=3663.521036 train[3]=27098.7871230094 test[3]=3664.538292 train[4]=26335.0948666773 test[4]=3607.732494 train[5]=25836.5231498885 test[5]=3562.667540 train[6]=26444.4391771873 test[6]=3608.909416 train[7]=26207.8906783764 test[7]=3624.418594 train[8]=26213.9611328383 test[8]=3625.911551 train[9]=25861.8336267897 test[9]=3583.917442 train[10]=28053.3775765046 test[10]=3767.399861 train[11]=25076.6453210967 test[11]=3394.604043 train[12]=26207.8906783764 test[12]=3624.418594 train[13]=26207.8906783764 test[13]=3624.418594 train[14]=30107.6358847518 test[14]=4154.201307 train[15]=25862.8619277157 test[15]=3562.258359 train[16]=26207.8906783764 test[16]=3624.418594 train[17]=26211.5859885288 test[17]=3579.485602 train[18]=25077.6000373159 test[18]=3394.597037 train[19]=26596.0496978005 test[19]=3666.730239 train[20]=26241.2455467601 test[20]=3585.920403 train[21]=26896.9821496187 test[21]=3576.258288 train[22]=24987.8934076518 test[22]=3377.222817 train[23]=25676.7063936708 test[23]=3485.480264 train[24]=25606.1807519296 test[24]=3475.830135 train[25]=25334.5354305563 test[25]=3398.857296 train[26]=25077.6000373160 test[26]=3394.597037 train[27]=26207.8906783764 test[27]=3624.418594 train[28]=26253.3884848305 test[28]=3531.511101 train[29]=26251.2044954294 test[29]=3530.722326 ============================================================= AVERAGES (TRAIN_MSE, TEST_MSE): 26248.598 3583.796 AVERAGES (TRAIN_RMSE, TEST_RMSE): 162.01419 59.864815
# Settings Tab: Advanced # Run Date: 06/09/2025 15:40:09 # Run Time: 03:18:12 (11892734 ms) @ Parameters: ============================================================ 1. Dataset File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/datasets/MLDATASETS-master/QFC_GUI_datasets/Regression/abalone.data 2. Dataset Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.train 3. Dataset Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.test 4. Export Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.train 5. Export Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.test 6. Export Cpp File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.cpp 7. Features: 1 8. Feature Create Model: Copy 9. Feature Evaluate Model: Neural 10. Test Iters: 30 11. Random Seed: 1 12. Threads: 1 13. Neural Weights: 10 14. Neural Training Method: Genetic 15. BFGS Iteration: 2001 16. GN Chromosomes: 500 17. GN Max Generations: 200 18. GN Selection Rate: 0,10 19. GN Mutation Rate: 0,05 20. GN Local Search Rate: 0,00 21. GN Local Search Method: BFGS @ Results: =============================================================== train[0]=17699.9586712188 test[0]=2119.426673 train[1]=17699.9586712188 test[1]=2119.426673 train[2]=17699.9586712188 test[2]=2119.426673 train[3]=17699.9586712188 test[3]=2119.426673 train[4]=17699.9586712188 test[4]=2119.426673 train[5]=17699.9586712188 test[5]=2119.426673 train[6]=17699.9586712188 test[6]=2119.426673 train[7]=17699.9586712188 test[7]=2119.426673 train[8]=17699.9586712188 test[8]=2119.426673 train[9]=17699.9586712188 test[9]=2119.426673 train[10]=17699.9586712188 test[10]=2119.426673 train[11]=17699.9586712188 test[11]=2119.426673 train[12]=17699.9586712188 test[12]=2119.426673 train[13]=17699.9586712188 test[13]=2119.426673 train[14]=17699.9586712188 test[14]=2119.426673 train[15]=17699.9586712188 test[15]=2119.426673 train[16]=17699.9586712188 test[16]=2119.426673 train[17]=17699.9586712188 test[17]=2119.426673 train[18]=17699.9586712188 test[18]=2119.426673 train[19]=17699.9586712188 test[19]=2119.426673 train[20]=17699.9586712188 test[20]=2119.426673 train[21]=17699.9586712188 test[21]=2119.426673 train[22]=17699.9586712188 test[22]=2119.426673 train[23]=17699.9586712188 test[23]=2119.426673 train[24]=17699.9586712188 test[24]=2119.426673 train[25]=17699.9586712188 test[25]=2119.426673 train[26]=17699.9586712188 test[26]=2119.426673 train[27]=17699.9586712188 test[27]=2119.426673 train[28]=17699.9586712188 test[28]=2119.426673 train[29]=17699.9586712188 test[29]=2119.426673 ============================================================= AVERAGES (TRAIN_MSE, TEST_MSE): 17699.959 2119.4267 AVERAGES (TRAIN_RMSE, TEST_RMSE): 133.04119 46.037231
# Settings Tab: Advanced # Run Date: 06/09/2025 19:14:17 # Run Time: 07:16:22 (26182921 ms) @ Parameters: ============================================================ 1. Dataset File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/datasets/MLDATASETS-master/QFC_GUI_datasets/Regression/abalone.data 2. Dataset Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.train 3. Dataset Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.test 4. Export Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.train 5. Export Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.test 6. Export Cpp File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.cpp 7. Features: 2 8. Feature Create Model: RBF 9. Feature Evaluate Model: RBF 10. Test Iters: 30 11. Random Seed: 1 12. Threads: 1 13. RBF Weights: 10 14. GE Chromosomes: 500 15. GE Max Generations: 200 16. GE Selection Rate: 0,10 17. GE Mutation Rate: 0,05 18. GE Length: 40 19. GE Local Search Generations: 20 20. GE Local Search Rate: 0,05 21. GE Local Search Method: Crossover 22. GE Method: Genetic @ Results: =============================================================== Iteration: 1 Best Fitness: -18450 Best program: f1(x)=((-76.56)*x4) f2(x)=((-41.54)*x8/((-4.67)*x6)) Iteration: 2 Best Fitness: -18108.3 Best program: f1(x)=((-234.702)/(-8.1))*x4+x3 f2(x)=((-41.54)*x8/((-4.67)*x6)) Iteration: 3 Best Fitness: -17207.1 Best program: f1(x)=((-5.13)*x5) f2(x)=((-41.54)*x8/((-4.67)*x6)) Iteration: 4 Best Fitness: -17061.2 Best program: f1(x)=((-5.13)*x5) f2(x)=((-41.54)*x8/((-4.87)*x6)) Iteration: 5 Best Fitness: -17061.2 Best program: f1(x)=((-5.13)*x5) f2(x)=((-41.54)*x8/((-4.87)*x6)) Iteration: 6 Best Fitness: -17060 Best program: f1(x)=((-8.13)*x5) f2(x)=((-41.54)*x8/(4.87*x6)) Iteration: 7 Best Fitness: -16816.1 Best program: f1(x)=((-5.13)*x5-sin(((-9.6)*x1*cos(((-7.398)*x4/((-193.0)*x8*(71.6*x2))))))) f2(x)=((-41.54)*x8/((-4.87)*x6)) Iteration: 8 Best Fitness: -16815 Best program: f1(x)=((-5.13)*x5-sin(((-9.6)*x1*cos(((-7.398)*x4/((-193.0)*x2*(71.6*x2))))))) f2(x)=((-41.54)*x8/(4.87*x6)) Iteration: 9 Best Fitness: -16815 Best program: f1(x)=((-5.13)*x5-sin(((-9.6)*x1*cos(((-7.398)*x4/((-193.0)*x2*(71.6*x2))))))) f2(x)=((-41.54)*x8/(4.87*x6)) Iteration: 10 Best Fitness: -16815 Best program: f1(x)=((-5.13)*x5-sin(((-9.6)*x1/cos(((-7.398)*x3/((-193.0)*x2*(71.6*x2))))))) f2(x)=((-41.54)*x8/(4.87*x6)) Iteration: 11 Best Fitness: -16746.5 Best program: f1(x)=((-5.13)*x5-sin(((-9.7)*x1*cos((7.398*x4/((-193.4)*x8*(71.6*x2))))))) f2(x)=((-41.04)*x8/((-4.87)*x6)) Iteration: 12 Best Fitness: -16744.8 Best program: f1(x)=((-5.13)*x5-sin(((-9.7)*x1*cos((7.398*x4/((-193.8)*x8*(71.6*x2))))))) f2(x)=((-41.53)*x8/(4.87*x6)) Iteration: 13 Best Fitness: -16742.3 Best program: f1(x)=((-5.13)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/((-193.0)*x7*(71.6*x2))))))) f2(x)=((-41.54)*x8/(4.88*x6)) Iteration: 14 Best Fitness: -16733.3 Best program: f1(x)=((-5.13)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/((-193.4)*x8*(71.6*x2))))))) f2(x)=((-41.54)*x8/((-4.87)*x6)) Iteration: 15 Best Fitness: -16733.2 Best program: f1(x)=((-5.13)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/((-193.0)*x8*(71.6*x2))))))) f2(x)=((-41.54)*x8/(4.87*x6)) Iteration: 16 Best Fitness: -16724.5 Best program: f1(x)=((-5.13)*x5-sin(((-9.7)*x1/cos(((-1.398)*x3/((-193.49)*x7/cos((x2-((-0.1)*x5))))))))) f2(x)=(41.54*x8/((-4.87)*x6)) Iteration: 17 Best Fitness: -16724.5 Best program: f1(x)=((-5.13)*x5-sin(((-9.7)*x1/cos(((-1.398)*x3/((-193.49)*x7/cos((x2-((-0.1)*x5))))))))) f2(x)=(41.54*x8/((-4.87)*x6)) Iteration: 18 Best Fitness: -16718 Best program: f1(x)=((-5.13)*x5-sin(((-9.7)*x1/cos(((-1.998)*x3/((-193.49)*x7/cos((x2-((-0.1)*x5))))))))) f2(x)=(41.54*x8/((-4.87)*x6)) Iteration: 19 Best Fitness: -16717.6 Best program: f1(x)=((-5.13)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/((-193.0)*x5)))))) f2(x)=((-41.54)*x8/(4.88*x6)) Iteration: 20 Best Fitness: -16705.3 Best program: f1(x)=((-5.23)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 21 Best Fitness: -16705.3 Best program: f1(x)=((-5.23)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 22 Best Fitness: -16705.3 Best program: f1(x)=((-5.23)*x5-sin(((-9.7)*x1/cos(((-7.394)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 23 Best Fitness: -16705.3 Best program: f1(x)=((-5.23)*x5-sin(((-9.7)*x1/cos(((-7.394)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 24 Best Fitness: -16705.3 Best program: f1(x)=((-5.23)*x5-sin(((-9.7)*x1/cos(((-7.394)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 25 Best Fitness: -16705.3 Best program: f1(x)=((-5.23)*x5-sin(((-9.7)*x1/cos(((-7.394)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 26 Best Fitness: -16703.4 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 27 Best Fitness: -16703.4 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 28 Best Fitness: -16703.4 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 29 Best Fitness: -16703.4 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 30 Best Fitness: -16703.4 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 31 Best Fitness: -16703.4 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 32 Best Fitness: -16703.4 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 33 Best Fitness: -16703.4 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 34 Best Fitness: -16703.4 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 35 Best Fitness: -16703.4 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 36 Best Fitness: -16703.4 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 37 Best Fitness: -16703.4 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 38 Best Fitness: -16703.4 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 39 Best Fitness: -16703.4 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 40 Best Fitness: -16703.4 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 41 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.398*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 42 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.398*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 43 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.398*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 44 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.398*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 45 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.398*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 46 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 47 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 48 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 49 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 50 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 51 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 52 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 53 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 54 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 55 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 56 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 57 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 58 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 59 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 60 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 61 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 62 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 63 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 64 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 65 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 66 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 67 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 68 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 69 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 70 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 71 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 72 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 73 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 74 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 75 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 76 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 77 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 78 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 79 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 80 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 81 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 82 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 83 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 84 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 85 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 86 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 87 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 88 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 89 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 90 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 91 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 92 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 93 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 94 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 95 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 96 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 97 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 98 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 99 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 100 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 101 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 102 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 103 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 104 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 105 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 106 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 107 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 108 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 109 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 110 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 111 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 112 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 113 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 114 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 115 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 116 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 117 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 118 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 119 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 120 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 121 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 122 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 123 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 124 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 125 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 126 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 127 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 128 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 129 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 130 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 131 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 132 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 133 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 134 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 135 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 136 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 137 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 138 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 139 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 140 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 141 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 142 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 143 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 144 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 145 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 146 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 147 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 148 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 149 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 150 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 151 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 152 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 153 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 154 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 155 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 156 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 157 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 158 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 159 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 160 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 161 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 162 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 163 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 164 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 165 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 166 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 167 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 168 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 169 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 170 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 171 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 172 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 173 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 174 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 175 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 176 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 177 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 178 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 179 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 180 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 181 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 182 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 183 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 184 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 185 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 186 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 187 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 188 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 189 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 190 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 191 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 192 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 193 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 194 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 195 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 196 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 197 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 198 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 199 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 200 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) train[0]=17586.2454605729 test[0]=2141.402397 train[1]=17798.7470694513 test[1]=2188.341165 train[2]=16729.3145122629 test[2]=1993.520600 train[3]=16849.5598696286 test[3]=2016.897845 train[4]=16732.8699090266 test[4]=2004.401124 train[5]=17584.0700867986 test[5]=2141.145401 train[6]=16727.9763796791 test[6]=1992.878929 train[7]=17587.4677064967 test[7]=2141.518347 train[8]=17358.0320158405 test[8]=2169.067924 train[9]=17397.8821146959 test[9]=2159.638230 train[10]=17337.4237468313 test[10]=2167.621912 train[11]=17582.1608796275 test[11]=2140.118157 train[12]=17425.9969509258 test[12]=2189.039892 train[13]=17341.2844534525 test[13]=2169.091278 train[14]=16717.3377388044 test[14]=1989.517442 train[15]=16720.6673865185 test[15]=1993.464002 train[16]=16714.0013353895 test[16]=1987.999340 train[17]=17437.8821304296 test[17]=2180.988957 train[18]=17422.6146406217 test[18]=2107.998417 train[19]=16771.2495163562 test[19]=1989.004936 train[20]=17564.6227547327 test[20]=2137.019703 train[21]=17716.7040073564 test[21]=2138.231060 train[22]=16717.9267049367 test[22]=1992.248273 train[23]=17415.6917019016 test[23]=2185.656957 train[24]=17564.6227547327 test[24]=2137.019703 train[25]=17306.8446154233 test[25]=2181.451494 train[26]=16873.8878958471 test[26]=1996.901106 train[27]=17564.6227547328 test[27]=2137.019703 train[28]=17584.0700867986 test[28]=2141.145401 train[29]=17564.6227547327 test[29]=2137.019703 ============================================================== AVERAGES (TRAIN_MSE, TEST_MSE): 17256.547 2101.579 AVERAGES (TRAIN_RMSE, TEST_RMSE): 131.36417 45.842982
# Settings Tab: Advanced # Run Date: 07/09/2025 19:14:17 # Run Time: 10:20:13 (37213825 ms) @ Parameters: ============================================================ 1. Dataset File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/datasets/MLDATASETS-master/QFC_GUI_datasets/Regression/abalone.data 2. Dataset Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/abalone.train 3. Dataset Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/abalone.test 4. Export Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/export.train 5. Export Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/export.test 6. Export Cpp File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/export.cpp 7. Features: 2 8. Feature Create Model: RBF 9. Feature Evaluate Model: Neural 10. Test Iters: 30 11. Random Seed: 1 12. Threads: 1 13. GE Chromosomes: 500 14. GE Max Generations: 200 15. GE Selection Rate: 0,10 16. GE Mutation Rate: 0,05 17. GE Length: 40 18. GE Local Search Generations: 20 19. GE Local Search Rate: 0,05 20. GE Local Search Method: Crossover 21. GE Method: Genetic 22. RBF Weights: 10 23. Neural Weights: 10 24. Neural Training Method: Genetic 25. BFGS Iteration: 2001 26. GN Chromosomes: 500 27. GN Max Generations: 200 28. GN Selection Rate: 0,10 29. GN Mutation Rate: 0,05 30. GN Local Search Rate: 0,00 31. GN Local Search Method: BFGS @ Results: =============================================================== Iteration: 1 Best Fitness: -18450 Best program: f1(x)=((-76.56)*x4) f2(x)=((-41.54)*x8/((-4.67)*x6)) Iteration: 2 Best Fitness: -18108.3 Best program: f1(x)=((-234.702)/(-8.1))*x4+x3 f2(x)=((-41.54)*x8/((-4.67)*x6)) Iteration: 3 Best Fitness: -17207.1 Best program: f1(x)=((-5.13)*x5) f2(x)=((-41.54)*x8/((-4.67)*x6)) Iteration: 4 Best Fitness: -17061.2 Best program: f1(x)=((-5.13)*x5) f2(x)=((-41.54)*x8/((-4.87)*x6)) Iteration: 5 Best Fitness: -17061.2 Best program: f1(x)=((-5.13)*x5) f2(x)=((-41.54)*x8/((-4.87)*x6)) Iteration: 6 Best Fitness: -17060 Best program: f1(x)=((-8.13)*x5) f2(x)=((-41.54)*x8/(4.87*x6)) Iteration: 7 Best Fitness: -16816.1 Best program: f1(x)=((-5.13)*x5-sin(((-9.6)*x1*cos(((-7.398)*x4/((-193.0)*x8*(71.6*x2))))))) f2(x)=((-41.54)*x8/((-4.87)*x6)) Iteration: 8 Best Fitness: -16815 Best program: f1(x)=((-5.13)*x5-sin(((-9.6)*x1*cos(((-7.398)*x4/((-193.0)*x2*(71.6*x2))))))) f2(x)=((-41.54)*x8/(4.87*x6)) Iteration: 9 Best Fitness: -16815 Best program: f1(x)=((-5.13)*x5-sin(((-9.6)*x1*cos(((-7.398)*x4/((-193.0)*x2*(71.6*x2))))))) f2(x)=((-41.54)*x8/(4.87*x6)) Iteration: 10 Best Fitness: -16815 Best program: f1(x)=((-5.13)*x5-sin(((-9.6)*x1/cos(((-7.398)*x3/((-193.0)*x2*(71.6*x2))))))) f2(x)=((-41.54)*x8/(4.87*x6)) Iteration: 11 Best Fitness: -16746.5 Best program: f1(x)=((-5.13)*x5-sin(((-9.7)*x1*cos((7.398*x4/((-193.4)*x8*(71.6*x2))))))) f2(x)=((-41.04)*x8/((-4.87)*x6)) Iteration: 12 Best Fitness: -16744.8 Best program: f1(x)=((-5.13)*x5-sin(((-9.7)*x1*cos((7.398*x4/((-193.8)*x8*(71.6*x2))))))) f2(x)=((-41.53)*x8/(4.87*x6)) Iteration: 13 Best Fitness: -16742.3 Best program: f1(x)=((-5.13)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/((-193.0)*x7*(71.6*x2))))))) f2(x)=((-41.54)*x8/(4.88*x6)) Iteration: 14 Best Fitness: -16733.3 Best program: f1(x)=((-5.13)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/((-193.4)*x8*(71.6*x2))))))) f2(x)=((-41.54)*x8/((-4.87)*x6)) Iteration: 15 Best Fitness: -16733.2 Best program: f1(x)=((-5.13)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/((-193.0)*x8*(71.6*x2))))))) f2(x)=((-41.54)*x8/(4.87*x6)) Iteration: 16 Best Fitness: -16724.5 Best program: f1(x)=((-5.13)*x5-sin(((-9.7)*x1/cos(((-1.398)*x3/((-193.49)*x7/cos((x2-((-0.1)*x5))))))))) f2(x)=(41.54*x8/((-4.87)*x6)) Iteration: 17 Best Fitness: -16724.5 Best program: f1(x)=((-5.13)*x5-sin(((-9.7)*x1/cos(((-1.398)*x3/((-193.49)*x7/cos((x2-((-0.1)*x5))))))))) f2(x)=(41.54*x8/((-4.87)*x6)) Iteration: 18 Best Fitness: -16718 Best program: f1(x)=((-5.13)*x5-sin(((-9.7)*x1/cos(((-1.998)*x3/((-193.49)*x7/cos((x2-((-0.1)*x5))))))))) f2(x)=(41.54*x8/((-4.87)*x6)) Iteration: 19 Best Fitness: -16717.6 Best program: f1(x)=((-5.13)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/((-193.0)*x5)))))) f2(x)=((-41.54)*x8/(4.88*x6)) Iteration: 20 Best Fitness: -16705.3 Best program: f1(x)=((-5.23)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 21 Best Fitness: -16705.3 Best program: f1(x)=((-5.23)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 22 Best Fitness: -16705.3 Best program: f1(x)=((-5.23)*x5-sin(((-9.7)*x1/cos(((-7.394)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 23 Best Fitness: -16705.3 Best program: f1(x)=((-5.23)*x5-sin(((-9.7)*x1/cos(((-7.394)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 24 Best Fitness: -16705.3 Best program: f1(x)=((-5.23)*x5-sin(((-9.7)*x1/cos(((-7.394)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 25 Best Fitness: -16705.3 Best program: f1(x)=((-5.23)*x5-sin(((-9.7)*x1/cos(((-7.394)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 26 Best Fitness: -16703.4 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 27 Best Fitness: -16703.4 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 28 Best Fitness: -16703.4 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 29 Best Fitness: -16703.4 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 30 Best Fitness: -16703.4 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 31 Best Fitness: -16703.4 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 32 Best Fitness: -16703.4 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 33 Best Fitness: -16703.4 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 34 Best Fitness: -16703.4 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 35 Best Fitness: -16703.4 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 36 Best Fitness: -16703.4 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 37 Best Fitness: -16703.4 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 38 Best Fitness: -16703.4 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 39 Best Fitness: -16703.4 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 40 Best Fitness: -16703.4 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos(((-7.398)*x3/(193.0*x5)))))) f2(x)=((-41.54)*x8/(4.89*x6)) Iteration: 41 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.398*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 42 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.398*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 43 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.398*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 44 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.398*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 45 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.398*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 46 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 47 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 48 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 49 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 50 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 51 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 52 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 53 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 54 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 55 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 56 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 57 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 58 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 59 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 60 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 61 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 62 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 63 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 64 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 65 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 66 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 67 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 68 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 69 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 70 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 71 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 72 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 73 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 74 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 75 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 76 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 77 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 78 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 79 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 80 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 81 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 82 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 83 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 84 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 85 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 86 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 87 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 88 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 89 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 90 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 91 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 92 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 93 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 94 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 95 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 96 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 97 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 98 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 99 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 100 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 101 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 102 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 103 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 104 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 105 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 106 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 107 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 108 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 109 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 110 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 111 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 112 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 113 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 114 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 115 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 116 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 117 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 118 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 119 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 120 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 121 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 122 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 123 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 124 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 125 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 126 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 127 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 128 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 129 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 130 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 131 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 132 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 133 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 134 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 135 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 136 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 137 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 138 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 139 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 140 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 141 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 142 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 143 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 144 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 145 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 146 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 147 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 148 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 149 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 150 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 151 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 152 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 153 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 154 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 155 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 156 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 157 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 158 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 159 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 160 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 161 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 162 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 163 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 164 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 165 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 166 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 167 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 168 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 169 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 170 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 171 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 172 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 173 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 174 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 175 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 176 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 177 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 178 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 179 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 180 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 181 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 182 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 183 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 184 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 185 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 186 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 187 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 188 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 189 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 190 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 191 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 192 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 193 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 194 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 195 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 196 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 197 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 198 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 199 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) Iteration: 200 Best Fitness: -16702.3 Best program: f1(x)=((-5.24)*x5-sin(((-9.7)*x1/cos((7.397*x3/(193.0*x5)))))) f2(x)=(41.57*x8/(4.89*x6)) train[0]=17199.8369578895 test[0]=1998.300283 train[1]=17199.8369578895 test[1]=1998.300283 train[2]=17199.8369578895 test[2]=1998.300283 train[3]=17199.8369578895 test[3]=1998.300283 train[4]=17199.8369578895 test[4]=1998.300283 train[5]=17199.8369578895 test[5]=1998.300283 train[6]=17199.8369578895 test[6]=1998.300283 train[7]=17199.8369578895 test[7]=1998.300283 train[8]=17199.8369578895 test[8]=1998.300283 train[9]=17199.8369578895 test[9]=1998.300283 train[10]=17199.8369578895 test[10]=1998.300283 train[11]=17199.8369578895 test[11]=1998.300283 train[12]=17199.8369578895 test[12]=1998.300283 train[13]=17199.8369578895 test[13]=1998.300283 train[14]=17199.8369578895 test[14]=1998.300283 train[15]=17199.8369578895 test[15]=1998.300283 train[16]=17199.8369578895 test[16]=1998.300283 train[17]=17199.8369578895 test[17]=1998.300283 train[18]=17199.8369578895 test[18]=1998.300283 train[19]=17199.8369578895 test[19]=1998.300283 train[20]=17199.8369578895 test[20]=1998.300283 train[21]=17199.8369578895 test[21]=1998.300283 train[22]=17199.8369578895 test[22]=1998.300283 train[23]=17199.8369578895 test[23]=1998.300283 train[24]=17199.8369578895 test[24]=1998.300283 train[25]=17199.8369578895 test[25]=1998.300283 train[26]=17199.8369578895 test[26]=1998.300283 train[27]=17199.8369578895 test[27]=1998.300283 train[28]=17199.8369578895 test[28]=1998.300283 train[29]=17199.8369578895 test[29]=1998.300283 ============================================================= AVERAGES (TRAIN_MSE, TEST_MSE): 17199.837 1998.3003 AVERAGES (TRAIN_RMSE, TEST_RMSE): 131.14815 44.702352
# Settings Tab: Advanced # Run Date: 04/09/2025 08:00:39 # Run Time: 00:00:04 (4348 ms) @ Parameters: ============================================================ 1. Dataset File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/datasets/MLDATASETS-master/QFC_GUI_datasets/Regression/mortgage.data 2. Dataset Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.train 3. Dataset Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.test 4. Export Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.train 5. Export Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.test 6. Export Cpp File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.cpp 7. Features: 1 8. Feature Create Model: Copy 9. Feature Evaluate Model: RBF 10. Test Iters: 30 11. Random Seed: 1 12. Threads: 1 13. RBF Weights: 10 @ Results: =============================================================== train[0]=1268.9890414715 test[0]=132.156846 train[1]=1289.9407795622 test[1]=142.072212 train[2]=1204.8216506783 test[2]=133.874704 train[3]=1268.9890414715 test[3]=132.156846 train[4]=1499.2237092982 test[4]=157.825833 train[5]=1721.4467904275 test[5]=187.437312 train[6]=1243.1632733701 test[6]=138.853516 train[7]=1329.8224573135 test[7]=136.084654 train[8]=1234.3076953079 test[8]=136.868677 train[9]=1179.3500325657 test[9]=129.055817 train[10]=1368.6907862186 test[10]=144.023195 train[11]=1167.3820602846 test[11]=128.774290 train[12]=1182.8477866766 test[12]=128.990002 train[13]=1498.5886799993 test[13]=157.740044 train[14]=1338.6020932355 test[14]=139.387633 train[15]=1424.0194903095 test[15]=151.292540 train[16]=1177.8020045236 test[16]=128.944011 train[17]=1448.2513786664 test[17]=155.164050 train[18]=1562.0223126830 test[18]=170.283557 train[19]=1162.7094520858 test[19]=128.147494 train[20]=1477.9462834871 test[20]=155.796133 train[21]=1221.0290128430 test[21]=136.025021 train[22]=1182.8477866766 test[22]=128.990002 train[23]=1424.0194903095 test[23]=151.292540 train[24]=1139.6684167537 test[24]=125.382807 train[25]=1329.8224573135 test[25]=136.084654 train[26]=1477.9462834871 test[26]=155.796133 train[27]=1329.8224573135 test[27]=136.084654 train[28]=1510.2050785183 test[28]=164.762718 train[29]=1424.0194903095 test[29]=151.292540 ============================================================= AVERAGES (TRAIN_MSE, TEST_MSE): 1336.2766 143.35468 AVERAGES (TRAIN_RMSE, TEST_RMSE): 36.555117 11.973082
# Settings Tab: Advanced # Run Date: 06/09/2025 09:45:11 # Run Time: 00:56:37 (3397563 ms) @ Parameters: ============================================================ 1. Dataset File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/datasets/MLDATASETS-master/QFC_GUI_datasets/Regression/mortgage.data 2. Dataset Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.train 3. Dataset Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.test 4. Export Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.train 5. Export Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.test 6. Export Cpp File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.cpp 7. Features: 1 8. Feature Create Model: Copy 9. Feature Evaluate Model: Neural 10. Test Iters: 30 11. Random Seed: 1 12. Threads: 1 13. Neural Weights: 10 14. Neural Training Method: Genetic 15. BFGS Iteration: 2001 16. GN Chromosomes: 500 17. GN Max Generations: 200 18. GN Selection Rate: 0,10 19. GN Mutation Rate: 0,05 20. GN Local Search Rate: 0,00 21. GN Local Search Method: BFGS @ Results: =============================================================== train[0]=256.2787688824 test[0]=31.627707 train[1]=256.2787688824 test[1]=31.627707 train[2]=256.2787688824 test[2]=31.627707 train[3]=256.2787688824 test[3]=31.627707 train[4]=256.2787688824 test[4]=31.627707 train[5]=256.2787688824 test[5]=31.627707 train[6]=256.2787688824 test[6]=31.627707 train[7]=256.2787688824 test[7]=31.627707 train[8]=256.2787688824 test[8]=31.627707 train[9]=256.2787688824 test[9]=31.627707 train[10]=256.2787688824 test[10]=31.627707 train[11]=256.2787688824 test[11]=31.627707 train[12]=256.2787688824 test[12]=31.627707 train[13]=256.2787688824 test[13]=31.627707 train[14]=256.2787688824 test[14]=31.627707 train[15]=256.2787688824 test[15]=31.627707 train[16]=256.2787688824 test[16]=31.627707 train[17]=256.2787688824 test[17]=31.627707 train[18]=256.2787688824 test[18]=31.627707 train[19]=256.2787688824 test[19]=31.627707 train[20]=256.2787688824 test[20]=31.627707 train[21]=256.2787688824 test[21]=31.627707 train[22]=256.2787688824 test[22]=31.627707 train[23]=256.2787688824 test[23]=31.627707 train[24]=256.2787688824 test[24]=31.627707 train[25]=256.2787688824 test[25]=31.627707 train[26]=256.2787688824 test[26]=31.627707 train[27]=256.2787688824 test[27]=31.627707 train[28]=256.2787688824 test[28]=31.627707 train[29]=256.2787688824 test[29]=31.627707 ============================================================= AVERAGES (TRAIN_MSE, TEST_MSE): 256.27877 31.627707 AVERAGES (TRAIN_RMSE, TEST_RMSE): 16.008709 5.6238516
# Settings Tab: Advanced # Run Date: 06/09/2025 11:02:27 # Run Time: 01:09:13 (4153918 ms) @ Parameters: ============================================================ 1. Dataset File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/datasets/MLDATASETS-master/QFC_GUI_datasets/Regression/mortgage.data 2. Dataset Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.train 3. Dataset Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.test 4. Export Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.train 5. Export Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.test 6. Export Cpp File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.cpp 7. Features: 2 8. Feature Create Model: RBF 9. Feature Evaluate Model: RBF 10. Test Iters: 30 11. Random Seed: 1 12. Threads: 1 13. GE Chromosomes: 500 14. GE Max Generations: 200 15. GE Selection Rate: 0,10 16. GE Mutation Rate: 0,05 17. GE Length: 40 18. GE Local Search Generations: 20 19. GE Local Search Rate: 0,05 20. GE Local Search Method: Crossover 21. GE Method: Genetic 22. RBF Weights: 10 @ Results: =============================================================== Iteration: 1 Best Fitness: -247.083 Best program: f1(x)=(70.470/(-84.567))*x11 f2(x)=(-14.4)*x11+(-3.48)*x6 Iteration: 2 Best Fitness: -240.887 Best program: f1(x)=(6.95*x3-(x6-(x3/(4.8*x12)))) f2(x)=(-14.4)*x11+(-3.48)*x6 Iteration: 3 Best Fitness: -203.827 Best program: f1(x)=(70.470/(-844.6))*x13 f2(x)=(-14.4)*x11+(-3.48)*x6 Iteration: 4 Best Fitness: -190.994 Best program: f1(x)=(74.420/(-844.6))*x13 f2(x)=(-14.4)*x11+(-4.46)*x6 Iteration: 5 Best Fitness: -180.198 Best program: f1(x)=(70.470/(-804.6))*x13 f2(x)=(-14.4)*x11+(-5.48)*x6 Iteration: 6 Best Fitness: -177.024 Best program: f1(x)=(4.687*x6) f2(x)=(-08.4)*x11+(-3.48)*x6 Iteration: 7 Best Fitness: -172.809 Best program: f1(x)=(4.689*x6) f2(x)=(-12.4)*x11+(-5.48)*x6 Iteration: 8 Best Fitness: -172.673 Best program: f1(x)=(4.639*x6) f2(x)=(-08.4)*x11+(-3.96)*x6 Iteration: 9 Best Fitness: -170.97 Best program: f1(x)=(4.189*x6) f2(x)=(-08.4)*x11+(-3.96)*x6 Iteration: 10 Best Fitness: -170.868 Best program: f1(x)=(4.189*x6) f2(x)=(-08.4)*x11+(-3.98)*x6 Iteration: 11 Best Fitness: -170.812 Best program: f1(x)=(4.182*x6) f2(x)=(-08.3)*x11+(-3.96)*x6 Iteration: 12 Best Fitness: -170.229 Best program: f1(x)=(4.285*x6) f2(x)=(-08.1)*x11+(-3.98)*x6 Iteration: 13 Best Fitness: -170.229 Best program: f1(x)=(4.285*x6) f2(x)=(-08.1)*x11+(-3.98)*x6 Iteration: 14 Best Fitness: -170.007 Best program: f1(x)=(4.235*x6) f2(x)=(-08.1)*x11+(-3.98)*x6 Iteration: 15 Best Fitness: -170.007 Best program: f1(x)=(4.235*x6) f2(x)=(-08.1)*x11+(-3.98)*x6 Iteration: 16 Best Fitness: -169.968 Best program: f1(x)=(4.226*x6) f2(x)=(-08.1)*x11+(-3.98)*x6 Iteration: 17 Best Fitness: -169.964 Best program: f1(x)=(4.225*x6) f2(x)=(-08.1)*x11+(-3.98)*x6 Iteration: 18 Best Fitness: -169.952 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.98)*x6 Iteration: 19 Best Fitness: -169.952 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.98)*x6 Iteration: 20 Best Fitness: -169.952 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.98)*x6 Iteration: 21 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 22 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 23 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 24 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 25 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 26 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 27 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 28 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 29 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 30 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 31 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 32 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 33 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 34 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 35 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 36 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 37 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 38 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 39 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 40 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 41 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 42 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 43 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 44 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 45 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 46 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 47 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 48 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 49 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 50 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 51 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 52 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 53 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 54 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 55 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 56 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 57 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 58 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 59 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 60 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 61 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 62 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 63 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 64 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 65 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 66 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 67 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 68 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 69 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 70 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 71 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 72 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 73 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 74 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 75 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 76 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 77 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 78 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 79 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 80 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 81 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 82 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 83 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 84 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 85 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 86 Best Fitness: -115.126 Best program: f1(x)=(04.22*x6/sin((6.75/(-35.45))*x4)) f2(x)=(-08.1)*x11+(-3.59)*x6 Iteration: 87 Best Fitness: -112.113 Best program: f1(x)=(04.22*x6/sin((6.75/(-35.45))*x4)) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 88 Best Fitness: -88.4968 Best program: f1(x)=(04.22*x6/sin((6.75/(-35.45))*x4)) f2(x)=(-08.1)*x4+(-3.995)*x5 Iteration: 89 Best Fitness: -58.6713 Best program: f1(x)=(04.22*x6/sin((6.75/(-35.45))*x4)) f2(x)=(-08.1)*x4+(-3.98)*x6 Iteration: 90 Best Fitness: -50.4624 Best program: f1(x)=(04.22*x6/sin((6.75/(-35.45))*x4)) f2(x)=(-08.1)*x5+(-3.99)*x6 Iteration: 91 Best Fitness: -38.5865 Best program: f1(x)=(04.32*x9/sin((6.76/(-357.53))*x10)) f2(x)=(-08.1)*x5+(-5.99)*x6 Iteration: 92 Best Fitness: -38.5865 Best program: f1(x)=(04.32*x9/sin((6.76/(-357.53))*x10)) f2(x)=(-08.1)*x5+(-5.99)*x6 Iteration: 93 Best Fitness: -37.8666 Best program: f1(x)=(04.22*x6/sin((6.75/(-354.53))*x10)) f2(x)=(-08.1)*x5+(-5.98)*x6 Iteration: 94 Best Fitness: -36.8402 Best program: f1(x)=(04.42*x6/sin((6.75/(-35.45))*x4)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 95 Best Fitness: -27.3551 Best program: f1(x)=(04.32*x6/sin((6.76/351.53)*x10)) f2(x)=(-08.1)*x5+(-5.96)*x6 Iteration: 96 Best Fitness: -27.3551 Best program: f1(x)=(04.32*x6/sin((6.76/351.53)*x10)) f2(x)=(-08.1)*x5+(-5.96)*x6 Iteration: 97 Best Fitness: -22.7841 Best program: f1(x)=(06.82*x6/sin((6.76/351.53)*x10)) f2(x)=(-08.1)*x5+(-5.96)*x6 Iteration: 98 Best Fitness: -22.4294 Best program: f1(x)=(04.32*x6/sin((6.76/351.52)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 99 Best Fitness: -22.351 Best program: f1(x)=(04.32*x6/sin((6.76/351.53)*x10)) f2(x)=(-05.1)*x5+(-3.95)*x6 Iteration: 100 Best Fitness: -22.0381 Best program: f1(x)=(04.32*x6/sin((6.76/351.33)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 101 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 102 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 103 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 104 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 105 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 106 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 107 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 108 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 109 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 110 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 111 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 112 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 113 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 114 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 115 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 116 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 117 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 118 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 119 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 120 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 121 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 122 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 123 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 124 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 125 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 126 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 127 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 128 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 129 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 130 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 131 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 132 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 133 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 134 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 135 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 136 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 137 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 138 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 139 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 140 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 141 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 142 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 143 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 144 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 145 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 146 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 147 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 148 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 149 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 150 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 151 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 152 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 153 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 154 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 155 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 156 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 157 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 158 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 159 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 160 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 161 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 162 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 163 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 164 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 165 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 166 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 167 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 168 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 169 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 170 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 171 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 172 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 173 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 174 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 175 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 176 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 177 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 178 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 179 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 180 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 181 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 182 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 183 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 184 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 185 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 186 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 187 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 188 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 189 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 190 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 191 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 192 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 193 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 194 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 195 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 196 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 197 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 198 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 199 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 200 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 train[0]=392.4716581322 test[0]=68.926807 train[1]=65.3003238738 test[1]=43.226337 train[2]=65.3003238738 test[2]=43.226337 train[3]=59.1816929430 test[3]=20.033621 train[4]=124.6191906515 test[4]=41.301946 train[5]=89.7771960777 test[5]=37.934131 train[6]=127.7557050844 test[6]=44.641646 train[7]=127.7557050844 test[7]=44.641646 train[8]=32.0800438817 test[8]=16.333137 train[9]=52.3420363628 test[9]=17.403368 train[10]=104.3146133864 test[10]=50.380269 train[11]=32.7107731777 test[11]=16.774979 train[12]=123.3560371100 test[12]=39.468196 train[13]=67.2947671212 test[13]=23.632696 train[14]=127.3585337666 test[14]=45.322726 train[15]=127.7557050844 test[15]=44.641646 train[16]=69.9666571551 test[16]=28.196439 train[17]=120.2660924924 test[17]=42.499115 train[18]=97.0775702905 test[18]=49.675088 train[19]=127.7557050844 test[19]=44.641646 train[20]=63.8406802677 test[20]=21.735265 train[21]=68.3701709320 test[21]=20.644265 train[22]=31.8873658116 test[22]=13.828811 train[23]=65.3003238738 test[23]=43.226337 train[24]=32.0800435193 test[24]=16.333050 train[25]=75.7651894990 test[25]=30.941818 train[26]=104.6928735195 test[26]=50.808832 train[27]=120.2660924924 test[27]=42.499115 train[28]=124.6191906515 test[28]=41.301946 train[29]=63.1644789311 test[29]=27.898397 ============================================================= AVERAGES (TRAIN_MSE, TEST_MSE): 96.147558 35.73732 AVERAGES (TRAIN_RMSE, TEST_RMSE): 9.8054861 5.97807
# Settings Tab: Advanced # Run Date: 06/09/2025 12:30:38 # Run Time: 01:43:18 (6198456 ms) @ Parameters: ============================================================ 1. Dataset File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/datasets/MLDATASETS-master/QFC_GUI_datasets/Regression/mortgage.data 2. Dataset Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.train 3. Dataset Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.test 4. Export Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.train 5. Export Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.test 6. Export Cpp File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.cpp 7. Features: 2 8. Feature Create Model: RBF 9. Feature Evaluate Model: Neural 10. Test Iters: 30 11. Random Seed: 1 12. Threads: 1 13. GE Chromosomes: 500 14. GE Max Generations: 200 15. GE Selection Rate: 0,10 16. GE Mutation Rate: 0,05 17. GE Length: 40 18. GE Local Search Generations: 20 19. GE Local Search Rate: 0,05 20. GE Local Search Method: Crossover 21. GE Method: Genetic 22. RBF Weights: 10 23. Neural Weights: 10 24. Neural Training Method: Genetic 25. BFGS Iteration: 2001 26. GN Chromosomes: 500 27. GN Max Generations: 200 28. GN Selection Rate: 0,10 29. GN Mutation Rate: 0,05 30. GN Local Search Rate: 0,00 31. GN Local Search Method: BFGS @ Results: =============================================================== Iteration: 1 Best Fitness: -247.083 Best program: f1(x)=(70.470/(-84.567))*x11 f2(x)=(-14.4)*x11+(-3.48)*x6 Iteration: 2 Best Fitness: -240.887 Best program: f1(x)=(6.95*x3-(x6-(x3/(4.8*x12)))) f2(x)=(-14.4)*x11+(-3.48)*x6 Iteration: 3 Best Fitness: -203.827 Best program: f1(x)=(70.470/(-844.6))*x13 f2(x)=(-14.4)*x11+(-3.48)*x6 Iteration: 4 Best Fitness: -190.994 Best program: f1(x)=(74.420/(-844.6))*x13 f2(x)=(-14.4)*x11+(-4.46)*x6 Iteration: 5 Best Fitness: -180.198 Best program: f1(x)=(70.470/(-804.6))*x13 f2(x)=(-14.4)*x11+(-5.48)*x6 Iteration: 6 Best Fitness: -177.024 Best program: f1(x)=(4.687*x6) f2(x)=(-08.4)*x11+(-3.48)*x6 Iteration: 7 Best Fitness: -172.809 Best program: f1(x)=(4.689*x6) f2(x)=(-12.4)*x11+(-5.48)*x6 Iteration: 8 Best Fitness: -172.673 Best program: f1(x)=(4.639*x6) f2(x)=(-08.4)*x11+(-3.96)*x6 Iteration: 9 Best Fitness: -170.97 Best program: f1(x)=(4.189*x6) f2(x)=(-08.4)*x11+(-3.96)*x6 Iteration: 10 Best Fitness: -170.868 Best program: f1(x)=(4.189*x6) f2(x)=(-08.4)*x11+(-3.98)*x6 Iteration: 11 Best Fitness: -170.812 Best program: f1(x)=(4.182*x6) f2(x)=(-08.3)*x11+(-3.96)*x6 Iteration: 12 Best Fitness: -170.229 Best program: f1(x)=(4.285*x6) f2(x)=(-08.1)*x11+(-3.98)*x6 Iteration: 13 Best Fitness: -170.229 Best program: f1(x)=(4.285*x6) f2(x)=(-08.1)*x11+(-3.98)*x6 Iteration: 14 Best Fitness: -170.007 Best program: f1(x)=(4.235*x6) f2(x)=(-08.1)*x11+(-3.98)*x6 Iteration: 15 Best Fitness: -170.007 Best program: f1(x)=(4.235*x6) f2(x)=(-08.1)*x11+(-3.98)*x6 Iteration: 16 Best Fitness: -169.968 Best program: f1(x)=(4.226*x6) f2(x)=(-08.1)*x11+(-3.98)*x6 Iteration: 17 Best Fitness: -169.964 Best program: f1(x)=(4.225*x6) f2(x)=(-08.1)*x11+(-3.98)*x6 Iteration: 18 Best Fitness: -169.952 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.98)*x6 Iteration: 19 Best Fitness: -169.952 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.98)*x6 Iteration: 20 Best Fitness: -169.952 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.98)*x6 Iteration: 21 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 22 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 23 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 24 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 25 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 26 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 27 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 28 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 29 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 30 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 31 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 32 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 33 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 34 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 35 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 36 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 37 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 38 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 39 Best Fitness: -169.921 Best program: f1(x)=(4.222*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 40 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 41 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 42 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 43 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 44 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 45 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 46 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 47 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 48 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 49 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 50 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 51 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 52 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 53 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 54 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 55 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 56 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 57 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 58 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 59 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 60 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 61 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 62 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 63 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 64 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 65 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 66 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 67 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 68 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 69 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 70 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 71 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 72 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 73 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 74 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 75 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 76 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 77 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 78 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 79 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 80 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 81 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 82 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 83 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 84 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 85 Best Fitness: -169.913 Best program: f1(x)=(04.22*x6) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 86 Best Fitness: -115.126 Best program: f1(x)=(04.22*x6/sin((6.75/(-35.45))*x4)) f2(x)=(-08.1)*x11+(-3.59)*x6 Iteration: 87 Best Fitness: -112.113 Best program: f1(x)=(04.22*x6/sin((6.75/(-35.45))*x4)) f2(x)=(-08.1)*x11+(-3.99)*x6 Iteration: 88 Best Fitness: -88.4968 Best program: f1(x)=(04.22*x6/sin((6.75/(-35.45))*x4)) f2(x)=(-08.1)*x4+(-3.995)*x5 Iteration: 89 Best Fitness: -58.6713 Best program: f1(x)=(04.22*x6/sin((6.75/(-35.45))*x4)) f2(x)=(-08.1)*x4+(-3.98)*x6 Iteration: 90 Best Fitness: -50.4624 Best program: f1(x)=(04.22*x6/sin((6.75/(-35.45))*x4)) f2(x)=(-08.1)*x5+(-3.99)*x6 Iteration: 91 Best Fitness: -38.5865 Best program: f1(x)=(04.32*x9/sin((6.76/(-357.53))*x10)) f2(x)=(-08.1)*x5+(-5.99)*x6 Iteration: 92 Best Fitness: -38.5865 Best program: f1(x)=(04.32*x9/sin((6.76/(-357.53))*x10)) f2(x)=(-08.1)*x5+(-5.99)*x6 Iteration: 93 Best Fitness: -37.8666 Best program: f1(x)=(04.22*x6/sin((6.75/(-354.53))*x10)) f2(x)=(-08.1)*x5+(-5.98)*x6 Iteration: 94 Best Fitness: -36.8402 Best program: f1(x)=(04.42*x6/sin((6.75/(-35.45))*x4)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 95 Best Fitness: -27.3551 Best program: f1(x)=(04.32*x6/sin((6.76/351.53)*x10)) f2(x)=(-08.1)*x5+(-5.96)*x6 Iteration: 96 Best Fitness: -27.3551 Best program: f1(x)=(04.32*x6/sin((6.76/351.53)*x10)) f2(x)=(-08.1)*x5+(-5.96)*x6 Iteration: 97 Best Fitness: -22.7841 Best program: f1(x)=(06.82*x6/sin((6.76/351.53)*x10)) f2(x)=(-08.1)*x5+(-5.96)*x6 Iteration: 98 Best Fitness: -22.4294 Best program: f1(x)=(04.32*x6/sin((6.76/351.52)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 99 Best Fitness: -22.351 Best program: f1(x)=(04.32*x6/sin((6.76/351.53)*x10)) f2(x)=(-05.1)*x5+(-3.95)*x6 Iteration: 100 Best Fitness: -22.0381 Best program: f1(x)=(04.32*x6/sin((6.76/351.33)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 101 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 102 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 103 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 104 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 105 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 106 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 107 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 108 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 109 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 110 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 111 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 112 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 113 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 114 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 115 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 116 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 117 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 118 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 119 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 120 Best Fitness: -21.9444 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.98)*x6 Iteration: 121 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 122 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 123 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 124 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 125 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 126 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 127 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 128 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 129 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 130 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 131 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 132 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 133 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 134 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 135 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 136 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 137 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 138 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 139 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 140 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 141 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 142 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 143 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 144 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 145 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 146 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 147 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 148 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 149 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 150 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 151 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 152 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 153 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 154 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 155 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 156 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 157 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 158 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 159 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 160 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 161 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 162 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 163 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 164 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 165 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 166 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 167 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 168 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 169 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 170 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 171 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 172 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 173 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 174 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 175 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 176 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 177 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 178 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 179 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 180 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 181 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 182 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 183 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 184 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 185 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 186 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 187 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 188 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 189 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 190 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 191 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 192 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 193 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 194 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 195 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 196 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 197 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 198 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 199 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 Iteration: 200 Best Fitness: -21.94 Best program: f1(x)=(04.32*x6/sin((6.76/351.38)*x10)) f2(x)=(-05.1)*x5+(-3.99)*x6 train[0]=150.0999088668 test[0]=24.551206 train[1]=150.0999088668 test[1]=24.551206 train[2]=150.0999088668 test[2]=24.551206 train[3]=150.0999088668 test[3]=24.551206 train[4]=150.0999088668 test[4]=24.551206 train[5]=150.0999088668 test[5]=24.551206 train[6]=150.0999088668 test[6]=24.551206 train[7]=150.0999088668 test[7]=24.551206 train[8]=150.0999088668 test[8]=24.551206 train[9]=150.0999088668 test[9]=24.551206 train[10]=150.0999088668 test[10]=24.551206 train[11]=150.0999088668 test[11]=24.551206 train[12]=150.0999088668 test[12]=24.551206 train[13]=150.0999088668 test[13]=24.551206 train[14]=150.0999088668 test[14]=24.551206 train[15]=150.0999088668 test[15]=24.551206 train[16]=150.0999088668 test[16]=24.551206 train[17]=150.0999088668 test[17]=24.551206 train[18]=150.0999088668 test[18]=24.551206 train[19]=150.0999088668 test[19]=24.551206 train[20]=150.0999088668 test[20]=24.551206 train[21]=150.0999088668 test[21]=24.551206 train[22]=150.0999088668 test[22]=24.551206 train[23]=150.0999088668 test[23]=24.551206 train[24]=150.0999088668 test[24]=24.551206 train[25]=150.0999088668 test[25]=24.551206 train[26]=150.0999088668 test[26]=24.551206 train[27]=150.0999088668 test[27]=24.551206 train[28]=150.0999088668 test[28]=24.551206 train[29]=150.0999088668 test[29]=24.551206 ============================================================= AVERAGES (TRAIN_MSE, TEST_MSE): 150.09991 24.551206 AVERAGES (TRAIN_RMSE, TEST_RMSE): 12.251527 4.9549174
# Settings Tab: Advanced # Run Date: 09/09/2025 15:02:53 # Run Time: 00:07:18 (438727 ms) @ Parameters: ============================================================ 1. Dataset File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/datasets/MLDATASETS-master/QFC_GUI_datasets/Regression/ailerons.data 2. Dataset Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.train 3. Dataset Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.test 4. Export Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.train 5. Export Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.test 6. Export Cpp File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.cpp 7. Features: 1 8. Feature Create Model: Copy 9. Feature Evaluate Model: RBF 10. Test Iters: 30 11. Random Seed: 1 12. Threads: 1 13. RBF Weights: 10 @ Results: =============================================================== train[0]=0.0020526498 test[0]=0.000230 train[1]=0.0020528884 test[1]=0.000230 train[2]=0.0020503656 test[2]=0.000230 train[3]=0.0020530989 test[3]=0.000230 train[4]=0.0020546017 test[4]=0.000230 train[5]=0.0020503656 test[5]=0.000230 train[6]=0.0020530989 test[6]=0.000230 train[7]=0.0020548189 test[7]=0.000230 train[8]=0.0020548045 test[8]=0.000230 train[9]=0.0020530989 test[9]=0.000230 train[10]=0.0020528194 test[10]=0.000230 train[11]=0.0020530989 test[11]=0.000230 train[12]=0.0020530989 test[12]=0.000230 train[13]=0.0020503656 test[13]=0.000230 train[14]=0.0020530989 test[14]=0.000230 train[15]=0.0020503656 test[15]=0.000230 train[16]=0.0020503656 test[16]=0.000230 train[17]=0.0020530989 test[17]=0.000230 train[18]=0.0020545534 test[18]=0.000230 train[19]=0.0020548189 test[19]=0.000230 train[20]=0.0020545806 test[20]=0.000230 train[21]=0.0020548189 test[21]=0.000230 train[22]=0.0020530989 test[22]=0.000230 train[23]=0.0020530989 test[23]=0.000230 train[24]=0.0020530989 test[24]=0.000230 train[25]=0.0020548189 test[25]=0.000230 train[26]=0.0020510860 test[26]=0.000230 train[27]=0.0020530989 test[27]=0.000230 train[28]=0.0020534887 test[28]=0.000230 train[29]=0.0020530989 test[29]=0.000230 ============================================================= AVERAGES (TRAIN_MSE, TEST_MSE): 0.0020529921 0.00022998795 AVERAGES (TRAIN_RMSE, TEST_RMSE): 0.045309956 0.015165354
# Settings Tab: Advanced # Run Date: 09/09/2025 15:50:27 # Run Time: 28:14:43 (101683045 ms) @ Parameters: ============================================================ 1. Dataset File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/datasets/MLDATASETS-master/QFC_GUI_datasets/Regression/ailerons.data 2. Dataset Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.train 3. Dataset Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/dataset.test 4. Export Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.train 5. Export Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.test 6. Export Cpp File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFc_UI/debug/exports/experiments/export.cpp 7. Features: 1 8. Feature Create Model: Copy 9. Feature Evaluate Model: Neural 10. Test Iters: 30 11. Random Seed: 1 12. Threads: 1 13. Neural Weights: 10 14. Neural Training Method: Genetic 15. BFGS Iteration: 2001 16. GN Chromosomes: 500 17. GN Max Generations: 200 18. GN Selection Rate: 0,10 19. GN Mutation Rate: 0,05 20. GN Local Search Rate: 0,00 21. GN Local Search Method: BFGS @ Results: =============================================================== train[0]=0.0003137038 test[0]=0.000036 train[1]=0.0003137038 test[1]=0.000036 train[2]=0.0003137038 test[2]=0.000036 train[3]=0.0003137038 test[3]=0.000036 train[4]=0.0003137038 test[4]=0.000036 train[5]=0.0003137038 test[5]=0.000036 train[6]=0.0003137038 test[6]=0.000036 train[7]=0.0003137038 test[7]=0.000036 train[8]=0.0003137038 test[8]=0.000036 train[9]=0.0003137038 test[9]=0.000036 train[10]=0.0003137038 test[10]=0.000036 train[11]=0.0003137038 test[11]=0.000036 train[12]=0.0003137038 test[12]=0.000036 train[13]=0.0003137038 test[13]=0.000036 train[14]=0.0003137038 test[14]=0.000036 train[15]=0.0003137038 test[15]=0.000036 train[16]=0.0003137038 test[16]=0.000036 train[17]=0.0003137038 test[17]=0.000036 train[18]=0.0003137038 test[18]=0.000036 train[19]=0.0003137038 test[19]=0.000036 train[20]=0.0003137038 test[20]=0.000036 train[21]=0.0003137038 test[21]=0.000036 train[22]=0.0003137038 test[22]=0.000036 train[23]=0.0003137038 test[23]=0.000036 train[24]=0.0003137038 test[24]=0.000036 train[25]=0.0003137038 test[25]=0.000036 train[26]=0.0003137038 test[26]=0.000036 train[27]=0.0003137038 test[27]=0.000036 train[28]=0.0003137038 test[28]=0.000036 train[29]=0.0003137038 test[29]=0.000036 ============================================================= AVERAGES (TRAIN_MSE, TEST_MSE): 0.00031370381 3.6162119e-05 AVERAGES (TRAIN_RMSE, TEST_RMSE): 0.017711686 0.0060134948
# Settings Tab: Advanced # Run Date: 10/09/2025 21:04:45 # Run Time: 211:50:38 (762638408 ms) @ Parameters: ============================================================ 1. Dataset File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFC_GUI/build/Desktop_Qt_5_15_2_MinGW_32_bit-Debug/debug/QFC_GUI_datasets/Regression/ailerons.data 2. Dataset Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFC_GUI/build/Desktop_Qt_5_15_2_MinGW_32_bit-Debug/debug/exports/ailerons.train 3. Dataset Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFC_GUI/build/Desktop_Qt_5_15_2_MinGW_32_bit-Debug/debug/exports/ailerons.test 4. Export Train File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFC_GUI/build/Desktop_Qt_5_15_2_MinGW_32_bit-Debug/debug/exports/export.train 5. Export Test File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFC_GUI/build/Desktop_Qt_5_15_2_MinGW_32_bit-Debug/debug/exports/export.test 6. Export Cpp File: C:/Users/Erarbo/Desktop/Diplomatiki/MyProjects/QFC_GUI/build/Desktop_Qt_5_15_2_MinGW_32_bit-Debug/debug/exports/export.cpp 7. Features: 2 8. Feature Create Model: RBF 9. Feature Evaluate Model: RBF 10. Test Iters: 30 11. Random Seed: 1 12. Threads: 6 13. GE Chromosomes: 500 14. GE Max Generations: 200 15. GE Selection Rate: 0,10 16. GE Mutation Rate: 0,05 17. GE Length: 40 18. GE Local Search Generations: 20 19. GE Local Search Rate: 0,05 20. GE Local Search Method: Crossover 21. GE Method: Genetic 22. RBF Weights: 10 @ Results: =============================================================== Iteration: 1 Best Fitness: -0.00088716 Best program: f1(x)=abs(cos((-06.61)*x15+sin(abs(94.3*x19)))) f2(x)=(x33-(48.100/68.63)*x7+994.84*x40) Iteration: 2 Best Fitness: -0.000728601 Best program: f1(x)=(x19+(067.7/(-030.920))*x7) f2(x)=(x33-(48.100/68.63)*x7+994.84*x40) Iteration: 3 Best Fitness: -0.000622079 Best program: f1(x)=x7 f2(x)=(x3-((-188.1)*x21)) Iteration: 4 Best Fitness: -0.000622079 Best program: f1(x)=x7 f2(x)=(x3-((-188.1)*x21)) Iteration: 5 Best Fitness: -0.000609159 Best program: f1(x)=x7 f2(x)=(x3-((-120.973)*x20)) Iteration: 6 Best Fitness: -0.000598913 Best program: f1(x)=(x7+((-013.62)*x13)) f2(x)=(x3-((-120.273)*x20)) Iteration: 7 Best Fitness: -0.000587434 Best program: f1(x)=(x7+((-019.4)*x17)) f2(x)=(x3-((-118.1)*x21)) Iteration: 8 Best Fitness: -0.000587434 Best program: f1(x)=(x7+((-019.4)*x17)) f2(x)=(x3-((-118.1)*x21)) Iteration: 9 Best Fitness: -0.000583024 Best program: f1(x)=x7 f2(x)=(x3-((-158.1)*x14)) Iteration: 10 Best Fitness: -0.000583011 Best program: f1(x)=(x7+((-019.464)*x34)) f2(x)=(x3-((-158.1)*x14)) Iteration: 11 Best Fitness: -0.000583011 Best program: f1(x)=(x7+((-019.464)*x34)) f2(x)=(x3-((-158.1)*x14)) Iteration: 12 Best Fitness: -0.000569386 Best program: f1(x)=x7+((-242.8)/(-3.0))*x28+97.8*x12+(59.90/(-45.115))*x37+(9.423/43.0)*x28 f2(x)=(x3-((-158.1)*x14)) Iteration: 13 Best Fitness: -0.000569386 Best program: f1(x)=x7+((-242.8)/(-3.0))*x28+97.8*x12+(59.90/(-45.115))*x37+(9.423/43.0)*x28 f2(x)=(x3-((-158.1)*x14)) Iteration: 14 Best Fitness: -0.000569312 Best program: f1(x)=x7+((-24.3)/43.0)*x28+97.8*x12+(59.90/(-45.815))*x37+(4.423/43.0)*x28 f2(x)=(x3-((-158.1)*x14)) Iteration: 15 Best Fitness: -0.000564765 Best program: f1(x)=x7+((-242.8)/(-3.077))*x17+log(x12+x12) f2(x)=(x3-((-158.1)*x14)) Iteration: 16 Best Fitness: -0.000559494 Best program: f1(x)=x7+((-342.8)/(-3.077))*x17+log(x12+x12) f2(x)=(x3-((-158.1)*x14)) Iteration: 17 Best Fitness: -0.000533188 Best program: f1(x)=x7+((-34.3)/43.0)*x7+97.8*x12+(59.90/(-45.115))*x37+(4.42/(-9.3))*x26 f2(x)=(x3-((-158.1)*x14)) Iteration: 18 Best Fitness: -0.000517073 Best program: f1(x)=x7+((-34.8)/43.0)*x7+97.8*x12+((-59.90)/(-45.115))*x37+(4.42/9.3)*x26 f2(x)=(x3-((-128.1)*x14)) Iteration: 19 Best Fitness: -0.000504846 Best program: f1(x)=x7+((-242.8)/(-3.077))*x9+x30 f2(x)=(x3-((-150.9)*x14)) Iteration: 20 Best Fitness: -0.000500217 Best program: f1(x)=x7+((-34.3)/47.0)*x7+97.8*x12+(59.13/(-45.115))*x37+(4.42/(-9.7))*x26 f2(x)=(x3-((-128.1)*x14)) Iteration: 21 Best Fitness: -0.000497297 Best program: f1(x)=x7+((-34.3)/49.2)*x7+97.8*x12+(593.0/(-55.615))*x37+((-4.42)/(-9.9))*x26 f2(x)=(x3-((-128.9)*x14)) Iteration: 22 Best Fitness: -0.000497297 Best program: f1(x)=x7+((-34.3)/49.2)*x7+97.8*x12+(593.0/(-55.615))*x37+((-4.42)/(-9.9))*x26 f2(x)=(x3-((-128.9)*x14)) Iteration: 23 Best Fitness: -0.000495675 Best program: f1(x)=x7+((-33.3)/49.2)*x7+97.1*x12+(593.0/(-45.615))*x37+(4.32/(-9.9))*x26 f2(x)=(x3-((-129.1)*x14)) Iteration: 24 Best Fitness: -0.000488745 Best program: f1(x)=x6+((-34.3)/44.2)*x7+97.8*x12+(598.6/(-45.215))*x37+(-4.42)*x4 f2(x)=(x3-((-128.1)*x14)) Iteration: 25 Best Fitness: -0.000424461 Best program: f1(x)=x6+((-24.3)/44.2)*x7+97.8*x12+(596.67/50.15)*x37+(-4.42)*x4 f2(x)=(x3-((-028.1)*x14)) Iteration: 26 Best Fitness: -0.000416689 Best program: f1(x)=x6+((-34.3)/44.2)*x7+(-97.8)*x12+(593.0/(-5.06))*x18+sin((-4.42)*x4) f2(x)=(x3-((-124.9)*x14)) Iteration: 27 Best Fitness: -0.000409606 Best program: f1(x)=x6+((-24.3)/44.2)*x7+97.8*x12+(596.67/50.15)*x37+(-2.42)*x4 f2(x)=(x3-((-028.1)*x14)) Iteration: 28 Best Fitness: -0.000401669 Best program: f1(x)=x6+((-24.3)/44.2)*x7+97.8*x12+(596.67/50.15)*x37+(-4.42)*x4 f2(x)=(x3-((-038.1)*x14)) Iteration: 29 Best Fitness: -0.00039567 Best program: f1(x)=x6+((-24.3)/44.2)*x7+97.8*x12+(596.67/50.15)*x37+(-2.42)*x4 f2(x)=(x3-((-038.1)*x14)) Iteration: 30 Best Fitness: -0.000389032 Best program: f1(x)=x6+((-24.3)/41.6)*x7+97.8*x12+(596.67/50.15)*x37+(-2.42)*x4 f2(x)=(x3-((-038.1)*x14)) Iteration: 31 Best Fitness: -0.000389032 Best program: f1(x)=x6+((-24.3)/41.6)*x7+97.8*x12+(596.67/50.15)*x37+(-2.42)*x4 f2(x)=(x3-((-038.1)*x14)) Iteration: 32 Best Fitness: -0.000389032 Best program: f1(x)=x6+((-24.3)/41.6)*x7+97.8*x12+(596.69/50.05)*x37+(-2.42)*x4 f2(x)=(x3-((-038.1)*x14)) Iteration: 33 Best Fitness: -0.000388713 Best program: f1(x)=x6+((-24.3)/41.6)*x7+97.8*x12+(996.67/50.15)*x37+(-2.42)*x4 f2(x)=(x3-((-038.1)*x14)) Iteration: 34 Best Fitness: -0.000388295 Best program: f1(x)=x6+((-24.3)/41.6)*x7+97.8*x12+(596.67/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.1)*x14)) Iteration: 35 Best Fitness: -0.000387811 Best program: f1(x)=x6+((-24.3)/41.6)*x7+97.8*x12+(996.67/90.15)*x37+(-2.42)*x4 f2(x)=(x3-((-038.139)*x17)) Iteration: 36 Best Fitness: -0.000387791 Best program: f1(x)=x6+((-24.3)/41.6)*x7+97.1*x12+(596.67/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.1)*x14)) Iteration: 37 Best Fitness: -0.000387564 Best program: f1(x)=x6+((-24.3)/41.6)*x7+97.1*x12+(596.67/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.4)*x14)) Iteration: 38 Best Fitness: -0.000387088 Best program: f1(x)=x6+((-24.3)/41.6)*x7+95.8*x12+(595.67/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.1)*x14)) Iteration: 39 Best Fitness: -0.000386987 Best program: f1(x)=x6+((-24.3)/41.6)*x7+95.8*x12+(596.67/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.1)*x14)) Iteration: 40 Best Fitness: -0.000386919 Best program: f1(x)=x6+((-24.3)/41.6)*x7+95.8*x12+(596.37/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.139)*x17)) Iteration: 41 Best Fitness: -0.000386901 Best program: f1(x)=x6+((-24.3)/41.6)*x7+95.4*x12+(596.67/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.1)*x14)) Iteration: 42 Best Fitness: -0.000386539 Best program: f1(x)=x6+((-24.3)/41.6)*x7+95.8*x12+(596.67/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.939)*x17)) Iteration: 43 Best Fitness: -0.000386343 Best program: f1(x)=x6+((-24.3)/41.6)*x7+95.8*x12+(595.77/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.931)*x17)) Iteration: 44 Best Fitness: -0.000386362 Best program: f1(x)=x6+((-24.3)/41.6)*x7+95.8*x12+(597.67/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.985)*x17)) Iteration: 45 Best Fitness: -0.000386359 Best program: f1(x)=x6+((-24.3)/41.6)*x7+95.8*x12+(596.67/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.985)*x17)) Iteration: 46 Best Fitness: -0.000386343 Best program: f1(x)=x6+((-24.3)/41.6)*x7+95.8*x12+(596.37/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.939)*x17)) Iteration: 47 Best Fitness: -0.000386343 Best program: f1(x)=x6+((-24.3)/41.6)*x7+95.8*x12+(596.37/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.939)*x17)) Iteration: 48 Best Fitness: -0.000386343 Best program: f1(x)=x6+((-24.3)/41.6)*x7+95.8*x12+(596.37/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.939)*x17)) Iteration: 49 Best Fitness: -0.000386343 Best program: f1(x)=x6+((-24.3)/41.6)*x7+95.8*x12+(596.37/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.939)*x17)) Iteration: 50 Best Fitness: -0.000386343 Best program: f1(x)=x6+((-24.3)/41.6)*x7+95.8*x12+(596.37/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.939)*x17)) Iteration: 51 Best Fitness: -0.000386343 Best program: f1(x)=x6+((-24.3)/41.6)*x7+95.8*x12+(596.37/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.939)*x17)) Iteration: 52 Best Fitness: -0.000386343 Best program: f1(x)=x6+((-24.3)/41.6)*x7+95.8*x12+(596.37/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.939)*x17)) Iteration: 53 Best Fitness: -0.000386343 Best program: f1(x)=x6+((-24.3)/41.6)*x7+95.8*x12+(596.37/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.939)*x17)) Iteration: 54 Best Fitness: -0.000386343 Best program: f1(x)=x6+((-24.3)/41.6)*x7+95.8*x12+(596.37/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.939)*x17)) Iteration: 55 Best Fitness: -0.000386343 Best program: f1(x)=x6+((-24.3)/41.6)*x7+95.8*x12+(596.37/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.939)*x17)) Iteration: 56 Best Fitness: -0.000386343 Best program: f1(x)=x6+((-24.3)/41.6)*x7+95.8*x12+(596.37/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.939)*x17)) Iteration: 57 Best Fitness: -0.000386343 Best program: f1(x)=x6+((-24.3)/41.6)*x7+95.8*x12+(596.37/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.939)*x17)) Iteration: 58 Best Fitness: -0.000386343 Best program: f1(x)=x6+((-24.3)/41.6)*x7+95.8*x12+(596.37/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.939)*x17)) Iteration: 59 Best Fitness: -0.000386343 Best program: f1(x)=x6+((-24.3)/41.6)*x7+95.8*x12+(596.37/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.939)*x17)) Iteration: 60 Best Fitness: -0.000386343 Best program: f1(x)=x6+((-24.3)/41.6)*x7+95.8*x12+(596.37/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.939)*x17)) Iteration: 61 Best Fitness: -0.000386343 Best program: f1(x)=x6+((-24.3)/41.6)*x7+95.8*x12+(596.37/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.938)*x17)) Iteration: 62 Best Fitness: -0.000386343 Best program: f1(x)=x6+((-24.3)/41.6)*x7+95.8*x12+(596.37/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.938)*x17)) Iteration: 63 Best Fitness: -0.000386343 Best program: f1(x)=x6+((-24.3)/41.6)*x7+95.8*x12+(596.37/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.938)*x17)) Iteration: 64 Best Fitness: -0.000352323 Best program: f1(x)=x6+((-24.3)/41.6)*x7+95.8*x12+(596.32/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.939)*x17-((-24.72)*x1*(x30*((-342.413)*x17))))) Iteration: 65 Best Fitness: -0.000351475 Best program: f1(x)=x6+((-24.3)/41.9)*x7+95.8*x12+(596.37/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.938)*x17)) Iteration: 66 Best Fitness: -0.000348438 Best program: f1(x)=x6+((-24.3)/41.6)*x7+93.8*x12+(596.81/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.933)*x17-((-24.72)*x1*(x30*((-342.413)*x17))))) Iteration: 67 Best Fitness: -0.000348438 Best program: f1(x)=x6+((-24.3)/41.6)*x7+93.8*x12+(596.81/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.933)*x17-((-24.72)*x1*(x30*((-342.413)*x17))))) Iteration: 68 Best Fitness: -0.000348067 Best program: f1(x)=x6+((-24.3)/41.6)*x7+93.8*x12+(596.81/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-034.939)*x17-((-24.72)*x1*(x30*((-342.413)*x17))))) Iteration: 69 Best Fitness: -0.000344269 Best program: f1(x)=x6+((-24.3)/41.6)*x7+95.8*x12+((-593.32)/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-038.939)*x17-((-34.92)*x1*(x30*((-442.973)*x17))))) Iteration: 70 Best Fitness: -0.000343412 Best program: f1(x)=x6+((-24.3)/41.6)*x7+95.8*x17+(595.32/5.215)*x37+(-2.42)*x4 f2(x)=(x3-((-033.939)*x17-((-74.72)*x1*(x30*((-342.436)*x17))))) Iteration: 71 Best Fitness: -0.00034197 Best program: f1(x)=x6+((-24.3)/41.6)*x7+92.8*x12+(5.203/54.275)*x37+(-2.42)*x4 f2(x)=(x3-((-038.939)*x17-((-64.72)*x1*(x30*((-442.413)*x17))))) Iteration: 72 Best Fitness: -0.000341969 Best program: f1(x)=x6+((-24.3)/41.6)*x7+92.8*x12+(5.603/54.275)*x37+(-2.42)*x4 f2(x)=(x3-((-038.939)*x17-((-64.72)*x1*(x30*((-442.973)*x17))))) Iteration: 73 Best Fitness: -0.000341825 Best program: f1(x)=x6+((-24.3)/41.6)*x7+91.8*x12+(5.203/54.275)*x37+(-2.42)*x4 f2(x)=(x3-((-038.939)*x17-((-64.72)*x1*(x30*((-442.413)*x17))))) Iteration: 74 Best Fitness: -0.00034144 Best program: f1(x)=x6+((-24.3)/41.9)*x7+91.8*x12+(592.3/46.215)*x37+(-2.42)*x4 f2(x)=(x3-((-033.939)*x17-((-64.72)*x1*(x30*((-442.413)*x17))))) Iteration: 75 Best Fitness: -0.000341393 Best program: f1(x)=x6+((-24.3)/41.9)*x7+91.8*x12+(592.3/461.15)*x37+(-2.42)*x4 f2(x)=(x3-((-033.939)*x17-((-64.72)*x1*(x30*((-442.418)*x17))))) Iteration: 76 Best Fitness: -0.000341393 Best program: f1(x)=x6+((-24.3)/41.9)*x7+91.8*x12+(592.3/461.15)*x37+(-2.42)*x4 f2(x)=(x3-((-033.939)*x17-((-64.72)*x1*(x30*((-442.418)*x17))))) Iteration: 77 Best Fitness: -0.000341206 Best program: f1(x)=x6+((-24.3)/41.6)*x7+93.2*x12+(5.308/(-45.825))*x37+(-2.42)*x4 f2(x)=(x3-((-033.934)*x17-((-74.92)*x1*(x30*((-442.133)*x17))))) Iteration: 78 Best Fitness: -0.000341076 Best program: f1(x)=x6+((-24.3)/41.0)*x7+93.8*x12 f2(x)=(x3-((-033.339)*x17-((-64.72)*x1*(x30*((-494.413)*x17))))) Iteration: 79 Best Fitness: -0.000340971 Best program: f1(x)=x6+((-23.3)/41.9)*x7+91.8*x12+((-596.3)/46.615)*x37+(-2.32)*x4 f2(x)=(x3-((-033.939)*x17-((-74.71)*x1*(x30*((-494.418)*x17))))) Iteration: 80 Best Fitness: -0.00034095 Best program: f1(x)=x6+((-23.3)/41.6)*x7+91.8*x12+(5.30/245.275)*x37+(-2.32)*x4 f2(x)=(x3-((-033.999)*x17-((-74.74)*x1*(x30*((-442.133)*x17))))) Iteration: 81 Best Fitness: -0.000340944 Best program: f1(x)=x6+((-23.3)/41.6)*x7+91.8*x12+(59.5/(-246.615))*x37+(-2.32)*x4 f2(x)=(x3-((-033.939)*x17-((-74.74)*x1*(x30*((-492.418)*x17))))) Iteration: 82 Best Fitness: -0.000340925 Best program: f1(x)=x6+((-23.3)/41.5)*x7+91.8*x12+(59.5/(-246.615))*x37+(-2.32)*x4 f2(x)=(x3-((-033.939)*x17-((-74.70)*x1*(x30*((-494.413)*x17))))) Iteration: 83 Best Fitness: -0.000340904 Best program: f1(x)=x6+((-23.3)/41.5)*x7+91.8*x12+((-595.3)/46.615)*x37+(-2.32)*x4 f2(x)=(x3-((-033.939)*x17-((-74.70)*x1*(x30*((-442.433)*x17))))) Iteration: 84 Best Fitness: -0.000340887 Best program: f1(x)=x6+((-23.3)/41.5)*x7+91.8*x12+((-595.3)/46.615)*x37+(-2.32)*x4 f2(x)=(x3-((-033.939)*x17-((-74.74)*x1*(x30*((-492.133)*x17))))) Iteration: 85 Best Fitness: -0.00034087 Best program: f1(x)=x6+((-23.3)/41.5)*x7+91.8*x12+((-995.3)/43.615)*x37+(-2.32)*x4 f2(x)=(x3-((-033.939)*x17-((-74.74)*x1*(x30*((-492.418)*x17))))) Iteration: 86 Best Fitness: -0.000340846 Best program: f1(x)=x6+((-23.3)/41.1)*x7+91.8*x12+((-799.0)/46.615)*x37+(-2.32)*x4 f2(x)=(x3-((-033.932)*x17-((-74.70)*x1*(x30*((-442.443)*x17))))) Iteration: 87 Best Fitness: -0.000340846 Best program: f1(x)=x6+((-23.3)/41.1)*x7+91.8*x12+((-799.0)/46.615)*x37+(-2.32)*x4 f2(x)=(x3-((-033.932)*x17-((-74.70)*x1*(x30*((-442.443)*x17))))) Iteration: 88 Best Fitness: -0.000340823 Best program: f1(x)=x6+((-23.3)/41.1)*x7+91.8*x12+((-799.0)/46.215)*x37+(-2.32)*x4 f2(x)=(x3-((-033.932)*x17-((-74.70)*x1*(x30*((-474.478)*x17))))) Iteration: 89 Best Fitness: -0.000340821 Best program: f1(x)=x6+((-23.3)/41.1)*x7+91.8*x12+((-795.0)/42.615)*x37+(-2.32)*x4 f2(x)=(x3-((-033.932)*x17-((-74.73)*x1*(x30*((-492.413)*x17))))) Iteration: 90 Best Fitness: -0.000340814 Best program: f1(x)=x6+((-23.3)/41.1)*x7+91.8*x12+((-995.3)/43.615)*x37+(-2.32)*x4 f2(x)=(x3-((-033.939)*x17-((-74.74)*x1*(x30*((-474.473)*x17))))) Iteration: 91 Best Fitness: -0.000340814 Best program: f1(x)=x6+((-23.3)/41.1)*x7+91.8*x12+((-995.3)/43.615)*x37+(-2.32)*x4 f2(x)=(x3-((-033.939)*x17-((-74.74)*x1*(x30*((-474.471)*x17))))) Iteration: 92 Best Fitness: -0.000340811 Best program: f1(x)=x6+((-23.3)/41.1)*x7+91.8*x12+((-79.506)/2.615)*x37+(-2.32)*x4 f2(x)=(x3-((-033.932)*x17-((-74.74)*x1*(x30*((-492.438)*x17))))) Iteration: 93 Best Fitness: -0.000340811 Best program: f1(x)=x6+((-23.3)/41.1)*x7+91.8*x12+((-79.506)/2.615)*x37+(-2.32)*x4 f2(x)=(x3-((-033.932)*x17-((-74.74)*x1*(x30*((-492.438)*x17))))) Iteration: 94 Best Fitness: -0.000340791 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.8*x12+((-799.0)/45.655)*x37+(-2.32)*x4 f2(x)=(x3-((-033.932)*x17-((-74.73)*x1*(x30*((-492.713)*x19))))) Iteration: 95 Best Fitness: -0.000340791 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.8*x12+((-799.0)/45.655)*x37+(-2.32)*x4 f2(x)=(x3-((-033.932)*x17-((-74.73)*x1*(x30*((-492.713)*x19))))) Iteration: 96 Best Fitness: -0.000340785 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.8*x12+((-999.0)/46.615)*x37+(-2.32)*x4 f2(x)=(x3-((-033.932)*x17-((-74.70)*x1*(x30*((-472.478)*x19))))) Iteration: 97 Best Fitness: -0.000340783 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.8*x12+((-999.0)/43.245)*x37+(-2.32)*x4 f2(x)=(x3-((-033.932)*x17-((-74.70)*x1*(x30*((-472.473)*x17))))) Iteration: 98 Best Fitness: -0.000340782 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.8*x12+((-995.3)/43.615)*x37+(-2.32)*x4 f2(x)=(x3-((-033.939)*x17-((-74.74)*x1*(x30*((-474.438)*x19))))) Iteration: 99 Best Fitness: -0.000340753 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.8*x12+((-795.1)/43.245)*x37+(-2.32)*x4 f2(x)=(x3-((-034.909)*x17-((-74.74)*x1*(x30*((-492.473)*x17))))) Iteration: 100 Best Fitness: -0.000340748 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.8*x12+((-795.1)/43.245)*x37+(-2.32)*x4 f2(x)=(x3-((-034.909)*x17-((-74.74)*x1*(x30*((-474.438)*x17))))) Iteration: 101 Best Fitness: -0.000340748 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.8*x12+((-795.1)/43.245)*x37+(-2.32)*x4 f2(x)=(x3-((-034.909)*x17-((-74.72)*x1*(x30*((-474.438)*x17))))) Iteration: 102 Best Fitness: -0.000340742 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.8*x12+((-995.3)/43.655)*x37+(-2.32)*x4 f2(x)=(x3-((-033.939)*x17-((-74.74)*x1*(x30*((-474.438)*x17))))) Iteration: 103 Best Fitness: -0.000340743 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.8*x12+((-995.3)/47.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.939)*x17-((-74.04)*x1*(x30*((-476.238)*x17))))) Iteration: 104 Best Fitness: -0.000340735 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.8*x12+((-99.016)/3.245)*x37+(-2.32)*x4 f2(x)=(x3-((-034.909)*x17-((-74.74)*x1*(x30*((-472.4)*x24))))) Iteration: 105 Best Fitness: -0.000340735 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.8*x12+((-99.016)/3.245)*x37+(-2.32)*x4 f2(x)=(x3-((-034.909)*x17-((-74.74)*x1*(x30*((-472.4)*x24))))) Iteration: 106 Best Fitness: -0.000340734 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.8*x12+((-995.3)/27.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.839)*x17-((-74.74)*x1*(x30*((-472.438)*x17))))) Iteration: 107 Best Fitness: -0.000340734 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.8*x12+((-995.3)/27.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.839)*x17-((-74.74)*x1*(x30*((-472.438)*x17))))) Iteration: 108 Best Fitness: -0.000340734 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.8*x12+((-995.3)/27.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.839)*x17-((-74.74)*x1*(x30*((-472.438)*x17))))) Iteration: 109 Best Fitness: -0.000340734 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.8*x12+((-972.3)/27.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.839)*x17-((-74.74)*x1*(x30*((-472.471)*x19))))) Iteration: 110 Best Fitness: -0.000340734 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.8*x12+((-972.3)/27.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.839)*x17-((-74.74)*x1*(x30*((-472.478)*x17))))) Iteration: 111 Best Fitness: -0.000340734 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.8*x12+((-972.3)/27.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.839)*x17-((-74.74)*x1*(x30*((-472.478)*x17))))) Iteration: 112 Best Fitness: -0.000340734 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.8*x12+((-972.3)/27.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.839)*x17-((-74.74)*x1*(x30*((-472.478)*x17))))) Iteration: 113 Best Fitness: -0.000340734 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.8*x12+((-972.3)/27.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.839)*x17-((-74.74)*x1*(x30*((-472.478)*x17))))) Iteration: 114 Best Fitness: -0.000340734 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.8*x12+((-972.3)/27.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.839)*x17-((-74.74)*x1*(x30*((-472.478)*x17))))) Iteration: 115 Best Fitness: -0.000340734 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.8*x12+((-995.3)/29.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.839)*x17-((-74.74)*x1*(x30*((-472.4)*x18))))) Iteration: 116 Best Fitness: -0.000340734 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.8*x12+((-995.3)/29.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.839)*x17-((-74.74)*x1*(x30*((-472.4)*x18))))) Iteration: 117 Best Fitness: -0.000340734 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.8*x12+((-995.3)/29.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.839)*x17-((-74.74)*x1*(x30*((-472.4)*x18))))) Iteration: 118 Best Fitness: -0.000340734 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.8*x12+((-995.3)/29.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.839)*x17-((-74.74)*x1*(x30*((-472.4)*x18))))) Iteration: 119 Best Fitness: -0.000340734 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.8*x12+((-995.3)/29.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.839)*x17-((-74.74)*x1*(x30*((-472.4)*x18))))) Iteration: 120 Best Fitness: -0.000340734 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.8*x12+((-995.3)/29.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.839)*x17-((-74.74)*x1*(x30*((-472.418)*x17))))) Iteration: 121 Best Fitness: -0.000340695 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-93.092)/2.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.809)*x17-((-74.61)*x1*(x30*((-474.4)*x18))))) Iteration: 122 Best Fitness: -0.000340695 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-93.092)/2.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.809)*x17-((-74.61)*x1*(x30*((-472.703)*x17))))) Iteration: 123 Best Fitness: -0.000340695 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-93.092)/2.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.809)*x17-((-74.61)*x1*(x30*((-472.418)*x23))))) Iteration: 124 Best Fitness: -0.000340694 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-93.092)/2.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.839)*x16-((-74.61)*x1*(x30*((-472.438)*x17))))) Iteration: 125 Best Fitness: -0.000340695 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-99.092)/2.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.839)*x16-((-74.61)*x1*(x30*((-472.488)*x17))))) Iteration: 126 Best Fitness: -0.000340695 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-99.092)/2.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.839)*x16-((-74.61)*x1*(x30*((-472.408)*x19))))) Iteration: 127 Best Fitness: -0.000340694 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-93.072)/2.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.839)*x17-((-74.61)*x1*(x30*((-472.713)*x19))))) Iteration: 128 Best Fitness: -0.000340694 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-93.092)/2.615)*x37+(-2.32)*x4 f2(x)=(x3-((-034.928)*x17-((-74.63)*x1*(x30*((-472.488)*x17))))) Iteration: 129 Best Fitness: -0.000340694 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-93.072)/2.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.869)*x17-((-74.61)*x1*(x30*((-472.733)*x17))))) Iteration: 130 Best Fitness: -0.000340694 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-93.072)/2.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.905)*x17-((-74.71)*x1*(x30*((-472.738)*x17))))) Iteration: 131 Best Fitness: -0.000340694 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-93.072)/2.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.905)*x17-((-74.71)*x1*(x30*((-472.738)*x17))))) Iteration: 132 Best Fitness: -0.000340637 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-93.072)/2.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.809)*x17-((-74.74)*x2*(x30*((-472.408)*x19))))) Iteration: 133 Best Fitness: -0.000340637 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-93.072)/2.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.809)*x17-((-74.74)*x2*(x30*((-472.408)*x19))))) Iteration: 134 Best Fitness: -0.000340637 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-93.072)/2.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.809)*x17-((-74.74)*x2*(x30*((-472.408)*x19))))) Iteration: 135 Best Fitness: -0.000340637 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-93.072)/2.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.809)*x17-((-74.74)*x2*(x30*((-472.408)*x19))))) Iteration: 136 Best Fitness: -0.000340637 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-93.072)/2.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.809)*x17-((-74.74)*x2*(x30*((-472.408)*x19))))) Iteration: 137 Best Fitness: -0.000340637 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-93.072)/2.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.809)*x17-((-74.74)*x2*(x30*((-472.408)*x19))))) Iteration: 138 Best Fitness: -0.000340542 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-99.012)/2.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.995)*x17-((-75.71)*x2*(x30*((-472.738)*x19))))) Iteration: 139 Best Fitness: -0.000340542 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-99.012)/2.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.995)*x17-((-75.71)*x2*(x30*((-472.738)*x19))))) Iteration: 140 Best Fitness: -0.000340542 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-99.012)/2.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.995)*x17-((-75.71)*x2*(x30*((-472.738)*x19))))) Iteration: 141 Best Fitness: -0.00034053 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-99.062)/2.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.195)*x17-((-75.71)*x2*(x30*((-242.738)*x19))))) Iteration: 142 Best Fitness: -0.000340542 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-99.012)/2.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.995)*x17-((-75.71)*x2*(x30*((-472.738)*x19))))) Iteration: 143 Best Fitness: -0.000340542 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-99.012)/2.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.995)*x17-((-75.71)*x2*(x30*((-472.738)*x19))))) Iteration: 144 Best Fitness: -0.000340509 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-83.070)/2.165)*x37+(-2.32)*x4 f2(x)=(x3-((-034.991)*x17-((-75.71)*x2*(x30*((-472.7)*x24))))) Iteration: 145 Best Fitness: -0.000340524 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-99.012)/2.615)*x37+(-2.32)*x4 f2(x)=(x3-((-034.995)*x17-((-75.71)*x2*(x30*((-472.738)*x19))))) Iteration: 146 Best Fitness: -0.000340524 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-99.012)/2.615)*x37+(-2.32)*x4 f2(x)=(x3-((-034.995)*x17-((-75.71)*x2*(x30*((-472.738)*x19))))) Iteration: 147 Best Fitness: -0.000340524 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-99.012)/2.615)*x37+(-2.32)*x4 f2(x)=(x3-((-034.995)*x17-((-75.71)*x2*(x30*((-472.738)*x19))))) Iteration: 148 Best Fitness: -0.000340524 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-99.012)/2.615)*x37+(-2.32)*x4 f2(x)=(x3-((-034.995)*x17-((-75.71)*x2*(x30*((-472.738)*x19))))) Iteration: 149 Best Fitness: -0.000340524 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-99.012)/2.615)*x37+(-2.32)*x4 f2(x)=(x3-((-034.995)*x17-((-75.71)*x2*(x30*((-472.738)*x19))))) Iteration: 150 Best Fitness: -0.000340524 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-99.012)/2.615)*x37+(-2.32)*x4 f2(x)=(x3-((-034.995)*x17-((-75.71)*x2*(x30*((-472.738)*x19))))) Iteration: 151 Best Fitness: -0.000340524 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-99.012)/2.615)*x37+(-2.32)*x4 f2(x)=(x3-((-034.995)*x17-((-75.71)*x2*(x30*((-472.738)*x19))))) Iteration: 152 Best Fitness: -0.000340524 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-99.012)/2.615)*x37+(-2.32)*x4 f2(x)=(x3-((-034.995)*x17-((-75.71)*x2*(x30*((-472.738)*x19))))) Iteration: 153 Best Fitness: -0.000340524 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-99.012)/2.615)*x37+(-2.32)*x4 f2(x)=(x3-((-034.995)*x17-((-75.71)*x2*(x30*((-472.738)*x19))))) Iteration: 154 Best Fitness: -0.000340524 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-99.012)/2.615)*x37+(-2.32)*x4 f2(x)=(x3-((-034.995)*x17-((-75.71)*x2*(x30*((-472.738)*x19))))) Iteration: 155 Best Fitness: -0.000340524 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-99.012)/2.615)*x37+(-2.32)*x4 f2(x)=(x3-((-034.995)*x17-((-75.71)*x2*(x30*((-472.738)*x19))))) Iteration: 156 Best Fitness: -0.000340524 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-99.012)/2.615)*x37+(-2.32)*x4 f2(x)=(x3-((-034.995)*x17-((-75.71)*x2*(x30*((-472.738)*x19))))) Iteration: 157 Best Fitness: -0.000340524 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-99.012)/2.615)*x37+(-2.32)*x4 f2(x)=(x3-((-034.995)*x17-((-75.71)*x2*(x30*((-472.738)*x19))))) Iteration: 158 Best Fitness: -0.000340524 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-99.012)/2.615)*x37+(-2.32)*x4 f2(x)=(x3-((-034.995)*x17-((-75.71)*x2*(x30*((-472.738)*x19))))) Iteration: 159 Best Fitness: -0.000340524 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-99.012)/2.615)*x37+(-2.32)*x4 f2(x)=(x3-((-034.995)*x17-((-75.71)*x2*(x30*((-472.738)*x19))))) Iteration: 160 Best Fitness: -0.000340502 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-98.291)/2.255)*x37+(-2.32)*x4 f2(x)=(x3-((-034.998)*x17-((-75.71)*x2*(x30*((-434.738)*x19))))) Iteration: 161 Best Fitness: -0.000340502 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-98.291)/2.255)*x37+(-2.32)*x4 f2(x)=(x3-((-034.998)*x17-((-75.71)*x2*(x30*((-434.738)*x19))))) Iteration: 162 Best Fitness: -0.000340502 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-98.291)/2.255)*x37+(-2.32)*x4 f2(x)=(x3-((-034.998)*x17-((-75.71)*x2*(x30*((-434.738)*x19))))) Iteration: 163 Best Fitness: -0.000340502 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-98.291)/2.255)*x37+(-2.32)*x4 f2(x)=(x3-((-034.998)*x17-((-75.71)*x2*(x30*((-434.738)*x19))))) Iteration: 164 Best Fitness: -0.000340502 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-98.291)/2.255)*x37+(-2.32)*x4 f2(x)=(x3-((-034.998)*x17-((-75.71)*x2*(x30*((-434.738)*x19))))) Iteration: 165 Best Fitness: -0.000340502 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-98.291)/2.255)*x37+(-2.32)*x4 f2(x)=(x3-((-034.998)*x17-((-75.71)*x2*(x30*((-434.738)*x19))))) Iteration: 166 Best Fitness: -0.000340502 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-98.291)/2.255)*x37+(-2.32)*x4 f2(x)=(x3-((-034.998)*x17-((-75.71)*x2*(x30*((-434.738)*x19))))) Iteration: 167 Best Fitness: -0.000340502 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-98.291)/2.255)*x37+(-2.32)*x4 f2(x)=(x3-((-034.998)*x17-((-75.71)*x2*(x30*((-434.738)*x19))))) Iteration: 168 Best Fitness: -0.000340502 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-98.291)/2.255)*x37+(-2.32)*x4 f2(x)=(x3-((-034.998)*x17-((-75.71)*x2*(x30*((-434.738)*x19))))) Iteration: 169 Best Fitness: -0.00034042 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-99.372)/6.615)*x37+(-2.32)*x4 f2(x)=(x3-((-034.901)*x17-((-75.71)*x2*(x30*((-372.772)*x19))))) Iteration: 170 Best Fitness: -0.00034042 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-99.372)/6.615)*x37+(-2.32)*x4 f2(x)=(x3-((-034.901)*x17-((-75.71)*x2*(x30*((-372.772)*x19))))) Iteration: 171 Best Fitness: -0.00034042 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-99.372)/6.615)*x37+(-2.32)*x4 f2(x)=(x3-((-034.901)*x17-((-75.71)*x2*(x30*((-372.772)*x19))))) Iteration: 172 Best Fitness: -0.00034042 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-99.372)/6.615)*x37+(-2.32)*x4 f2(x)=(x3-((-034.901)*x17-((-75.71)*x2*(x30*((-372.772)*x19))))) Iteration: 173 Best Fitness: -0.00034042 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-99.372)/6.615)*x37+(-2.32)*x4 f2(x)=(x3-((-034.901)*x17-((-75.71)*x2*(x30*((-372.772)*x19))))) Iteration: 174 Best Fitness: -0.00034042 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-99.372)/6.615)*x37+(-2.32)*x4 f2(x)=(x3-((-034.901)*x17-((-75.71)*x2*(x30*((-372.772)*x19))))) Iteration: 175 Best Fitness: -0.000340365 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-19.07)/86.645)*x37+(-2.32)*x4 f2(x)=(x3-((-034.901)*x17-((-75.71)*x2*(x30*((-372.772)*x19))))) Iteration: 176 Best Fitness: -0.000340329 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-19.07)/86.645)*x37+(-2.32)*x4 f2(x)=(x3-((-034.901)*x17-((-75.71)*x2*(x30*((-372.708)*x19))))) Iteration: 177 Best Fitness: -0.000340329 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-19.07)/86.645)*x37+(-2.32)*x4 f2(x)=(x3-((-034.901)*x17-((-75.71)*x2*(x30*((-372.708)*x19))))) Iteration: 178 Best Fitness: -0.000340329 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-19.07)/86.645)*x37+(-2.32)*x4 f2(x)=(x3-((-034.901)*x17-((-75.71)*x2*(x30*((-372.708)*x19))))) Iteration: 179 Best Fitness: -0.000340329 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-19.07)/86.645)*x37+(-2.32)*x4 f2(x)=(x3-((-034.901)*x17-((-75.71)*x2*(x30*((-372.708)*x19))))) Iteration: 180 Best Fitness: -0.000340329 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-19.07)/86.645)*x37+(-2.32)*x4 f2(x)=(x3-((-034.901)*x17-((-75.71)*x2*(x30*((-372.708)*x19))))) Iteration: 181 Best Fitness: -0.000340329 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-19.07)/86.645)*x37+(-2.32)*x4 f2(x)=(x3-((-034.901)*x17-((-75.71)*x2*(x30*((-372.708)*x19))))) Iteration: 182 Best Fitness: -0.000340314 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-19.07)/86.645)*x37+(-2.32)*x4 f2(x)=(x3-((-034.901)*x17-((-75.71)*x2*(x30*((-372.738)*x19))))) Iteration: 183 Best Fitness: -0.000340314 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-19.07)/86.645)*x37+(-2.32)*x4 f2(x)=(x3-((-034.901)*x17-((-75.71)*x2*(x30*((-372.738)*x19))))) Iteration: 184 Best Fitness: -0.000340314 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-19.07)/86.645)*x37+(-2.32)*x4 f2(x)=(x3-((-034.901)*x17-((-75.71)*x2*(x30*((-372.738)*x19))))) Iteration: 185 Best Fitness: -0.000340314 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-19.07)/86.645)*x37+(-2.32)*x4 f2(x)=(x3-((-034.901)*x17-((-75.71)*x2*(x30*((-372.738)*x19))))) Iteration: 186 Best Fitness: -0.000340314 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-19.07)/86.645)*x37+(-2.32)*x4 f2(x)=(x3-((-034.901)*x17-((-75.71)*x2*(x30*((-372.738)*x19))))) Iteration: 187 Best Fitness: -0.000340314 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-19.07)/86.645)*x37+(-2.32)*x4 f2(x)=(x3-((-034.901)*x17-((-75.71)*x2*(x30*((-372.738)*x19))))) Iteration: 188 Best Fitness: -0.000340314 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-19.07)/86.645)*x37+(-2.32)*x4 f2(x)=(x3-((-034.901)*x17-((-75.71)*x2*(x30*((-372.738)*x19))))) Iteration: 189 Best Fitness: -0.000340314 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-19.07)/86.645)*x37+(-2.32)*x4 f2(x)=(x3-((-034.901)*x17-((-75.71)*x2*(x30*((-372.738)*x19))))) Iteration: 190 Best Fitness: -0.000340314 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-19.07)/86.645)*x37+(-2.32)*x4 f2(x)=(x3-((-034.901)*x17-((-75.71)*x2*(x30*((-372.738)*x19))))) Iteration: 191 Best Fitness: -0.000340314 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-19.07)/86.645)*x37+(-2.32)*x4 f2(x)=(x3-((-034.901)*x17-((-75.71)*x2*(x30*((-372.738)*x19))))) Iteration: 192 Best Fitness: -0.000340314 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-19.07)/86.645)*x37+(-2.32)*x4 f2(x)=(x3-((-034.901)*x17-((-75.71)*x2*(x30*((-372.738)*x19))))) Iteration: 193 Best Fitness: -0.000340312 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-19.07)/84.255)*x37+(-2.32)*x4 f2(x)=(x3-((-034.901)*x17-((-79.01)*x2*(x30*((-372.708)*x19))))) Iteration: 194 Best Fitness: -0.000340312 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-19.07)/84.255)*x37+(-2.32)*x4 f2(x)=(x3-((-034.901)*x17-((-79.01)*x2*(x30*((-372.708)*x19))))) Iteration: 195 Best Fitness: -0.000340308 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-19.67)/87.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.901)*x17-((-75.21)*x2*(x30*((-376.738)*x19))))) Iteration: 196 Best Fitness: -0.000340308 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-19.67)/87.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.901)*x17-((-75.21)*x2*(x30*((-376.738)*x19))))) Iteration: 197 Best Fitness: -0.000340308 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-19.67)/87.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.901)*x17-((-75.21)*x2*(x30*((-376.738)*x19))))) Iteration: 198 Best Fitness: -0.000340308 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-19.67)/87.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.901)*x17-((-75.21)*x2*(x30*((-376.738)*x19))))) Iteration: 199 Best Fitness: -0.000340308 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-19.67)/87.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.901)*x17-((-75.21)*x2*(x30*((-376.738)*x19))))) Iteration: 200 Best Fitness: -0.000340308 Best program: f1(x)=x6+((-23.3)/41.1)*x7+90.1*x12+((-19.67)/87.655)*x37+(-2.32)*x4 f2(x)=(x3-((-034.901)*x17-((-75.21)*x2*(x30*((-376.738)*x19))))) train[0]=0.0003407294 test[0]=0.000038 train[1]=0.0003403092 test[1]=0.000038 train[2]=0.0003404335 test[2]=0.000038 train[3]=0.0003404207 test[3]=0.000038 train[4]=0.0003403121 test[4]=0.000038 train[5]=0.0003403386 test[5]=0.000038 train[6]=0.0003403129 test[6]=0.000038 train[7]=0.0003403285 test[7]=0.000038 train[8]=0.0003403110 test[8]=0.000038 train[9]=0.0003404718 test[9]=0.000038 train[10]=0.0003404231 test[10]=0.000038 train[11]=0.0003403840 test[11]=0.000038 train[12]=0.0003403118 test[12]=0.000038 train[13]=0.0003403150 test[13]=0.000038 train[14]=0.0003403277 test[14]=0.000038 train[15]=0.0003403091 test[15]=0.000038 train[16]=0.0003403626 test[16]=0.000038 train[17]=0.0003409022 test[17]=0.000038 train[18]=0.0003409022 test[18]=0.000038 train[19]=0.0003403140 test[19]=0.000038 train[20]=0.0003404032 test[20]=0.000038 train[21]=0.0003403114 test[21]=0.000038 train[22]=0.0003403082 test[22]=0.000038 train[23]=0.0003403403 test[23]=0.000038 train[24]=0.0003403212 test[24]=0.000038 train[25]=0.0003403117 test[25]=0.000038 train[26]=0.0003403086 test[26]=0.000038 train[27]=0.0003403083 test[27]=0.000038 train[28]=0.0003403085 test[28]=0.000038 train[29]=0.0003403078 test[29]=0.000038 ============================================================= AVERAGES (TRAIN_MSE, TEST_MSE): 0.00034039162 3.8211114e-05 AVERAGES (TRAIN_RMSE, TEST_RMSE): 0.018449705 0.0061815139
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