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Evolutionary algorithms dynamics and its hidden complex network structures

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dc.title Evolutionary algorithms dynamics and its hidden complex network structures en
dc.contributor.author Zelinka, Ivan
dc.contributor.author Davendra, Donald David
dc.contributor.author Lampinen, Jouni
dc.contributor.author Šenkeřík, Roman
dc.contributor.author Pluháček, Michal
dc.relation.ispartof Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
dc.identifier.isbn 9781479914883
dc.date.issued 2014
dc.citation.spage 3246
dc.citation.epage 3251
dc.event.title 2014 IEEE Congress on Evolutionary Computation, CEC 2014
dc.event.location Beijing
utb.event.state-en China
utb.event.state-cs Čína
dc.event.sdate 2014-07-06
dc.event.edate 2014-07-11
dc.type article
dc.language.iso en
dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
dc.identifier.doi 10.1109/CEC.2014.6900441
dc.relation.uri http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6900441
dc.description.abstract In this participation, we are continuing to show mutual intersection of two completely different areas of research: Complex networks and evolutionary computation. Large-scale networks, exhibiting complex patterns of interaction amongst vertices exist in both nature and man-made systems (i.e., communication networks, genetic pathways, ecological or economical networks, social networks, networks of various scientific collaboration etc.) and are a part of our daily life. We demonstrate that dynamics of evolutionary algorithms, that are based on Darwin theory of evolution and Mendel theory of genetic heritage, can be also visualized as complex networks. Such network can be then analyzed by means of classical tools of complex networks science. Results presented here are currently numerical demonstration rather than theoretical mathematical proofs. We open question whether evolutionary algorithms really create complex network structures and whether this knowledge can be successfully used like feedback for control of evolutionary dynamics and its improvement in order to increase the performance of evolutionary algorithms. © 2014 IEEE. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1004563
utb.identifier.obdid 43871916
utb.identifier.scopus 2-s2.0-84908577262
utb.identifier.wok 000356684604067
utb.source j-wok
dc.date.accessioned 2015-05-28T11:39:22Z
dc.date.available 2015-05-28T11:39:22Z
utb.contributor.internalauthor Šenkeřík, Roman
utb.contributor.internalauthor Pluháček, Michal
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