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dc.contributor.authorTsoulos, I. G.en
dc.contributor.authorLagaris, I. E.en
dc.date.accessioned2015-11-24T17:01:54Z-
dc.date.available2015-11-24T17:01:54Z-
dc.identifier.issn0010-4655-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/10994-
dc.rightsDefault Licence-
dc.subjectgenetic algorithmen
dc.subjectgenetic programmingen
dc.subjectgrammatical evolutionen
dc.subjectglobal optimizationen
dc.subjectstochastic methodsen
dc.subjectstopping ruleen
dc.subjectcontrolled random searchen
dc.subjectdifferential evolutionen
dc.subjectgenerationen
dc.titleGenMin: An enhanced genetic algorithm for global optimizationen
heal.typejournalArticle-
heal.type.enJournal articleen
heal.type.elΆρθρο Περιοδικούel
heal.identifier.primaryDOI 10.1016/j.cpc.2008.01.040-
heal.languageen-
heal.accesscampus-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικήςel
heal.publicationDate2008-
heal.abstractA new method that employs grammatical evolution and a stopping rule for finding the global minimum of a continuous multidimensional, multimodal function is considered. The genetic algorithm used is a hybrid genetic algorithm in conjunction with a local search procedure. We list results from numerical experiments with a series of test functions and we compare with other established global optimization methods. The accompanying software accepts objective functions coded either in Fortran 77 or in C++.en
heal.journalNameComputer Physics Communicationsen
heal.journalTypepeer reviewed-
heal.fullTextAvailabilityTRUE-
Appears in Collections:Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά)

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