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Hybridization of chaotic systems and success-history based adaptive differential evolution

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dc.title Hybridization of chaotic systems and success-history based adaptive differential evolution en
dc.contributor.author Viktorin, Adam
dc.contributor.author Šenkeřík, Roman
dc.contributor.author Pluháček, Michal
dc.relation.ispartof Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartof Hybrid Metaheuristics (HM 2016)
dc.identifier.issn 0302-9743 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn 978-3-319-39635-4
dc.identifier.isbn 978-3-319-39636-1
dc.date.issued 2016
utb.relation.volume 9668
dc.citation.spage 145
dc.citation.epage 156
dc.event.title 10th International Workshop on Hybrid Metaheuristics, HM 2016
dc.event.location Plymouth
utb.event.state-en England
utb.event.state-cs Anglie
dc.event.sdate 2016-06-08
dc.event.edate 2016-06-10
dc.type conferenceObject
dc.language.iso en
dc.publisher Springer Verlag
dc.identifier.doi 10.1007/978-3-319-39636-1_11
dc.relation.uri https://link.springer.com/chapter/10.1007/978-3-319-39636-1_11
dc.subject Deterministic chaos en
dc.subject Optimization en
dc.subject Parent selection en
dc.subject Pseudo-random number generator en
dc.subject Success-history based adaptive differential evolution en
dc.description.abstract This research paper focuses on hybridization of two soft computing fields – chaos theory and evolutionary algorithms, specifically on the implementation of Chaotic map based Pseudo-Random Number Generator (CPRNG) into the process of parent selection in Success-History Based Adaptive Differential Evolution (SHADE) algorithm. The impact on performance of the algorithm is tested on CEC2015 benchmark set where five different chaotic maps are used for random integer generation. Performance comparison shows that there is a potential in replacing classic Pseudo-Random Number Generators (PRNGs) with chaotic ones. The results provided in this paper show that the choice of CPRNG for given problem is crucial in terms of affecting the performance of the algorithm, therefore the next research step will be focused on the development of the framework which will adapt to the solved problem and select the most suitable CPRNG or their combination. © Springer International Publishing Switzerland 2016. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1006549
utb.identifier.obdid 43876087
utb.identifier.scopus 2-s2.0-84976644441
utb.identifier.wok 000379305500011
utb.source d-scopus
dc.date.accessioned 2016-08-09T14:02:57Z
dc.date.available 2016-08-09T14:02:57Z
utb.contributor.internalauthor Viktorin, Adam
utb.contributor.internalauthor Šenkeřík, Roman
utb.contributor.internalauthor Pluháček, Michal
utb.fulltext.affiliation Adam Viktorin, Roman Senkerik, Michal Pluhacek Faculty of Applied Informatics, Tomas Bata University in Zlin, T. G. Masaryka 5555, 760 01 Zlin, Czech Republic {aviktorin,senkerik,pluhacek}@fai.utb.cz
utb.fulltext.dates -
utb.fulltext.faculty Faculty of Applied Informatics
utb.fulltext.faculty Faculty of Applied Informatics
utb.fulltext.faculty Faculty of Applied Informatics
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