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On the influence of different randomization and complex network analysis for differential evolution

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dc.title On the influence of different randomization and complex network analysis for differential evolution en
dc.contributor.author Šenkeřík, Roman
dc.contributor.author Viktorin, Adam
dc.contributor.author Pluháček, Michal
dc.contributor.author Janoštík, Jakub
dc.contributor.author Davendra, Donald David
dc.relation.ispartof 2016 IEEE Congress on Evolutionary Computation, CEC 2016
dc.identifier.isbn 9781509006229
dc.date.issued 2016
dc.citation.spage 3346
dc.citation.epage 3353
dc.event.title 2016 IEEE Congress on Evolutionary Computation, CEC 2016
dc.event.location Vancouver
utb.event.state-en Canada
utb.event.state-cs Kanada
dc.event.sdate 2016-07-24
dc.event.edate 2016-07-29
dc.type conferenceObject
dc.language.iso en
dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
dc.identifier.doi 10.1109/CEC.2016.7744213
dc.relation.uri http://ieeexplore.ieee.org/document/7744213/
dc.subject Clustering coefficient en
dc.subject Complex networks en
dc.subject Degree centrality en
dc.subject Deterministic chaos en
dc.subject Differential evolution en
dc.subject Randomization en
dc.description.abstract This research deals with the hybridization of the chaos driven heuristics concept and complex networks framework for evolutionary algorithms. This paper aims on the experimental investigations on the influence of different randomization types for chaos-driven Differential Evolution (DE) through the analysis of complex network as a record of population dynamics. The population is visualized as an evolving complex network, which exhibits non-trivial features. Complex network attributes such as adjacency graph gives interconnectivity, centralities give the overview of convergence and stagnation, clustering coefficient gives diversity of population whereas other attributes like network density, average number of neighbors within the population shows efficiency of the network. Experiments were performed for two different DE strategies, four different randomization types and two simple test functions. © 2016 IEEE. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1006960
utb.identifier.obdid 43876352
utb.identifier.scopus 2-s2.0-85008249074
utb.identifier.wok 000390749103069
utb.source d-wok
dc.date.accessioned 2017-07-13T14:50:26Z
dc.date.available 2017-07-13T14:50:26Z
utb.contributor.internalauthor Šenkeřík, Roman
utb.contributor.internalauthor Viktorin, Adam
utb.contributor.internalauthor Pluháček, Michal
utb.contributor.internalauthor Janoštík, Jakub
utb.fulltext.affiliation Roman Senkerik, Adam Viktorin, Michal Pluhacek, Jakub Janostik Faculty of Applied Informatics Tomas Bata University in Zlin T.G. Masaryka 5555, 760 01 Zlin, Czech Republic {senkerik, pluhacek, aviktorin, janostik}@fai.utb.cz Donald Davendra Computer Science Department Central Washington University 400 E. University Way, Ellensburg, WA 98926-7520, USA. donaldd@cwu.edu
utb.fulltext.dates -
utb.fulltext.sponsorship This work was supported by Grant Agency of the Czech Republic - GACR P103/15/06700S, further by This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Pro- gramme project No. LO1303 (MSMT-7778/2014) and also 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 project No. IGA/CebiaTech/2016/007.
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