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Differential evolution with preferential interaction network

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dc.title Differential evolution with preferential interaction network en
dc.contributor.author Krömer, Pavel
dc.contributor.author Kudělka, Miloš
dc.contributor.author Šenkeřík, Roman
dc.contributor.author Pluháček, Michal
dc.relation.ispartof 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings
dc.identifier.isbn 978-1-5090-4601-0
dc.date.issued 2017
dc.citation.spage 1916
dc.citation.epage 1923
dc.event.title 2017 IEEE Congress on Evolutionary Computation, CEC 2017
utb.event.state-en Spain
utb.event.state-cs Španělsko
dc.event.sdate 2017-06-05
dc.event.edate 2017-06-08
dc.type conferenceObject
dc.language.iso en
dc.publisher Institute of Electrical and Electronics Engineers Inc.
dc.identifier.doi 10.1109/CEC.2017.7969535
dc.relation.uri http://ieeexplore.ieee.org/abstract/document/7969535/
dc.subject differential evolution en
dc.subject preferential attachment en
dc.subject competition en
dc.subject experiments en
dc.description.abstract Population-based metaheuristic optimization methods are built upon an algorithmic implementation of different types of complex dynamic behaviours. The problem-solving strategies they implement are often inspired by various natural and social phenomena whose fundamental principles were adopted for the use in practical search and optimization problems. New insights into complex systems, attained among others within the fields of network science and social network analysis, can be successfully incorporated into the study of evolutionary and swarm methods and used to improve their efficiency. Preferential attachment is a principle governing the growth of many real-world networks. That makes it a natural candidate for the use with network-based models of artificial evolution. Differential evolution is a widely-used evolutionary algorithm valued for its efficiency and versatility as well as simplicity and ease of implementation. In this paper, a variant of differential evolution, guided by an auxiliary model of population dynamics built with the help of the preferential attachment principle, is designed. The efficiency of the proposed approach is analyzed on the CEC 2017 real-parameter optimization benchmark. © 2017 IEEE. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1007490
utb.identifier.obdid 43877309
utb.identifier.scopus 2-s2.0-85028508044
utb.identifier.wok 000426929700248
utb.source d-scopus
dc.date.accessioned 2017-10-16T14:43:38Z
dc.date.available 2017-10-16T14:43:38Z
dc.description.sponsorship Czech Science Foundation [GA15-06700S]; projects of the Student Grant System, VSB-Technical University of Ostrava [SP2017/100, SP2017/85]; Ministry of Education of the Czech Republic [MSMT-7778/2014]; European Regional Development Fund under the Project CEBIA-Tech [CZ.1.05/2.1.00/03.0089]
utb.contributor.internalauthor Šenkeřík, Roman
utb.contributor.internalauthor Pluháček, Michal
utb.fulltext.affiliation Pavel Kromer, Miloš Kudělka Dept. of Computer Science VŠB - Technical University of Ostrava Ostrava, Czech Republic Email: {pavel.kromer, milos.kudelka}@vsb.cz Faculty of Applied Informatics Roman Senkerik, Michal Pluhacek Tomas Bata University in Zlin Nam T.G. Masaryka 5555, 760 01 Zlin, Czech Republic Email: {senkerik,pluhacek}@fai.utb.cz
utb.fulltext.dates -
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utb.fulltext.sponsorship This work was supported by the Czech Science Foundation under the grant no. GA15-06700S, by projects SP2017/100 and SP2017/85 of the Student Grant System, VSB-Technical University of Ostrava, by financial support of the research project NPU I No. MSMT-7778/2014 funded by the Ministry of Education of the Czech Republic, and also by the European Regional Development Fund under the Project CEBIA-Tech No. CZ.1.05/2.1.00/03.0089
utb.scopus.affiliation Dept. of Computer Science, VŠB - Technical University of Ostrava, Ostrava, Czech Republic; Faculty of Applied Informatics, Tomas Bata University in Zlin, Nam T.G. Masaryka 5555, Zlin, Czech Republic
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