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https://olympias.lib.uoi.gr/jspui/handle/123456789/11020Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Petalas, Y. G. | en |
| dc.contributor.author | Parsopoulos, K. E. | en |
| dc.contributor.author | Vrahatis, M. N. | en |
| dc.date.accessioned | 2015-11-24T17:02:10Z | - |
| dc.date.available | 2015-11-24T17:02:10Z | - |
| dc.identifier.issn | 1432-7643 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/11020 | - |
| dc.rights | Default Licence | - |
| dc.subject | fuzzy cognitive maps | en |
| dc.subject | memetic algorithms | en |
| dc.subject | particle swarm optimization | en |
| dc.subject | local search | en |
| dc.subject | machine learning | en |
| dc.subject | supervisory control-systems | en |
| dc.subject | global optimization | en |
| dc.subject | parameter selection | en |
| dc.subject | genetic algorithm | en |
| dc.subject | convergence | en |
| dc.subject | computation | en |
| dc.subject | network | en |
| dc.subject | search | en |
| dc.subject | design | en |
| dc.title | Improving fuzzy cognitive maps learning through memetic particle swarm optimization | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.identifier.primary | DOI 10.1007/s00500-008-0311-2 | - |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής | el |
| heal.publicationDate | 2009 | - |
| heal.abstract | Fuzzy cognitive maps constitute a neuro-fuzzy modeling methodology that can simulate complex systems accurately. Although their configuration is defined by experts, learning schemes based on evolutionary and swarm intelligence algorithms have been employed for improving their efficiency and effectiveness. This paper comprises an extensive study of the recently proposed swarm intelligence memetic algorithm that combines particle swarm optimization with both deterministic and stochastic local search schemes, for fuzzy cognitive maps learning tasks. Also, a new technique for the adaptation of the memetic schemes, with respect to the available number of function evaluations per application of the local search, is proposed. The memetic learning schemes are applied on four real-life problems and compared with established learning methods based on the standard particle swarm optimization, differential evolution, and genetic algorithms, justifying their superiority. | en |
| heal.journalName | Soft Computing | en |
| heal.journalType | peer reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Petalas-2009-Improving fuzzy cogn.pdf | 425.73 kB | Adobe PDF | View/Open Request a copy |
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