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Entropy driven self-adaptive differential evolution

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dc.title Entropy driven self-adaptive differential evolution en
dc.contributor.author Běhal, Ladislav
dc.contributor.author Vlček, Karel
dc.relation.ispartof MENDEL 2008
dc.identifier.isbn 978-80-214-3675-6
dc.date.issued 2008
dc.citation.spage 38
dc.citation.epage 43
dc.event.title 14th International Conference on Soft Computing
dc.event.location Brno
utb.event.state-en Czech Republic
utb.event.state-cs Česká republika
dc.event.sdate 2008-06-18
dc.event.edate 2008-06-20
dc.type conferenceObject
dc.language.iso en
dc.publisher Vysoké učení technické v Brně cs
dc.subject entropy en
dc.subject Gaussian entropy en
dc.subject linear entropy en
dc.subject differential evolution en
dc.subject self-adapting en
dc.description.abstract Self-adaptiveDE is one of the most efficient and popular modifications of evolution algorithm (Differential Evolution). The study deals with the possibility of using the entropy evaluation of population for SaDE algorithm modification, with an impact on its convergence towards global extreme. Further, the unavoidable increase of computational complexity is discussed. The paper proposes an algorithm based on the efficiency principles of SaDE algorithm. This proposed algorithm uses the above mentioned entropy calculation. In order to increase the algorithm efficiency, a recursive differential evolution was applied; it modifies its scale factors to gain the best convergence abilities, with respect to its robustness. Detailed performance comparisons of this algorithm on the currently used benchmark-functions are included. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1001871
utb.identifier.obdid 43857488
utb.identifier.scopus 2-s2.0-84898865204
utb.identifier.wok 000265681300008
utb.source d-wok
dc.date.accessioned 2011-08-09T07:34:07Z
dc.date.available 2011-08-09T07:34:07Z
utb.contributor.internalauthor Běhal, Ladislav
utb.contributor.internalauthor Vlček, Karel
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