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Differential Evolution and Chaotic Series

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dc.title Differential Evolution and Chaotic Series en
dc.contributor.author Šenkeřík, Roman
dc.contributor.author Viktorin, Adam
dc.contributor.author Pluháček, Michal
dc.contributor.author Kadavý, Tomáš
dc.contributor.author Komínková Oplatková, Zuzana
dc.relation.ispartof International Conference on Systems, Signals, and Image Processing
dc.identifier.issn 2157-8672 OCLC, Ulrich, Sherpa/RoMEO, JCR
dc.identifier.isbn 9781538669792
dc.date.issued 2018
utb.relation.volume 2018-June
dc.event.title 25th International Conference on Systems, Signals and Image Processing, IWSSIP 2018
dc.event.location Maribor
utb.event.state-en Slovenia
utb.event.state-cs Slovinsko
dc.event.sdate 2018-06-20
dc.event.edate 2018-06-22
dc.type conferenceObject
dc.language.iso en
dc.publisher IEEE Computer Society
dc.identifier.doi 10.1109/IWSSIP.2018.8439199
dc.relation.uri https://ieeexplore.ieee.org/document/8439199
dc.subject Chaotic map en
dc.subject Complex dynamics en
dc.subject Deterministic chaos en
dc.subject Differential Evolution en
dc.subject Population diversity en
dc.description.abstract This research deals with the modern and popular hybridization of chaotic dynamics and evolutionary computation. It is aimed at the influence of chaotic sequences on the performance of four selected Differential Evolution (DE) variants. The variants of interest were: original DE/Rand/1/ and DE/Best/1/ mutation schemes, simple parameter adaptive jDE, and the recent state of the art version SHADE. Experiments are focused on the extensive investigation of the different randomization schemes for the selection of individuals in DE algorithm driven by the nine different two-dimensional discrete chaotic systems, as the chaotic pseudo-random number generators. The performances of DE variants and their chaotic/non-chaotic versions are recorded in the one-dimensional settings of 10 $D$ and 15 test functions from the CEC 2015 benchmark. © 2018 IEEE. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1008211
utb.identifier.scopus 2-s2.0-85053130042
utb.source d-scopus
dc.date.accessioned 2018-10-03T11:13:03Z
dc.date.available 2018-10-03T11:13:03Z
utb.contributor.internalauthor Šenkeřík, Roman
utb.contributor.internalauthor Viktorin, Adam
utb.contributor.internalauthor Pluháček, Michal
utb.contributor.internalauthor Kadavý, Tomáš
utb.contributor.internalauthor Komínková Oplatková, Zuzana
utb.fulltext.affiliation Roman Senkerik, Adam Viktorin, Michal Pluhacek, Tomas Kadavy, and Zuzana Kominkova Oplatkova Faculty of Applied Informatics, Tomas Bata University in Zlin, Nam T.G. Masaryka 5555, 760 01 Zlin, Czech Republic senkerik@utb.cz
utb.fulltext.dates -
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utb.fulltext.sponsorship This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme Project no. LO1303 (MSMT-7778/2014), further by the European Regional Development Fund under the Project CEBIA-Tech no. CZ.1.05/2.1.00/03.0089 and by Internal Grant Agency of Tomas Bata University under the Projects no. IGA/CebiaTech/2018/003. This work is also based upon support by COST (European Cooperation in Science & Technology) under Action CA15140, Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO), and Action IC1406, High-Performance Modelling, and Simulation for Big Data Applications (cHiPSet). The work was further supported by resources of A.I.Lab at the Faculty of Applied Informatics, Tomas Bata University in Zlin (ailab.fai.utb.cz).
utb.scopus.affiliation Faculty of Applied Informatics, Tomas Bata University in Zlin, Nam T.G. Masaryka 5555, Zlin, 760 01, Czech Republic
utb.fulltext.projects LO1303 (MSMT-7778/2014)
utb.fulltext.projects CZ.1.05/2.1.00/03.0089
utb.fulltext.projects IGA/CebiaTech/2018/003
utb.fulltext.projects CA15140
utb.fulltext.projects ImAppNIO
utb.fulltext.projects IC1406
utb.fulltext.projects cHiPSet
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