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On the common population diversity measures in metaheuristics and their limitations

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dc.title On the common population diversity measures in metaheuristics and their limitations en
dc.contributor.author Pluháček, Michal
dc.contributor.author Viktorin, Adam
dc.contributor.author Kadavý, Tomáš
dc.contributor.author Kazíková, Anežka
dc.relation.ispartof 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings
dc.identifier.isbn 978-1-72819-048-8
dc.date.issued 2021
dc.event.title 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021
dc.event.location Orlando, FL
utb.event.state-en United States
utb.event.state-cs Spojené státy americké
dc.event.sdate 2021-12-05
dc.event.edate 2021-12-07
dc.type conferenceObject
dc.language.iso en
dc.publisher Institute of Electrical and Electronics Engineers Inc.
dc.identifier.doi 10.1109/SSCI50451.2021.9660135
dc.relation.uri https://ieeexplore.ieee.org/document/9660135
dc.subject population en
dc.subject diversity en
dc.subject metaheuristics en
dc.subject visualization en
dc.description.abstract Maintaining population diversity is one of the fundamental challenges for metaheuristic algorithms. With the emergence of adaptive and self-adaptive methods, the population diversity is frequently used as an indicator of the population state and feedback for the adaptive mechanism. In literature, several methods for quantification of the population diversity were proposed over the years. However, expressing the overall complexity of a metaheuristic population state by a single number inherently leads to simplification and distortion. As we show in this paper, lower diversity value does not automatically mean worse conditions for the emerging of new feasible solutions. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1010888
utb.identifier.obdid 43883359
utb.identifier.scopus 2-s2.0-85125774657
utb.identifier.wok 000824464300312
utb.source d-scopus
dc.date.accessioned 2022-03-21T08:23:44Z
dc.date.available 2022-03-21T08:23:44Z
dc.description.sponsorship IGA/CebiaTech/2021/001
dc.description.sponsorship Internal Grant Agency of Tomas Bata University [IGA/CebiaTech/2021/001]
utb.contributor.internalauthor Pluháček, Michal
utb.contributor.internalauthor Viktorin, Adam
utb.contributor.internalauthor Kadavý, Tomáš
utb.contributor.internalauthor Kazíková, Anežka
utb.fulltext.affiliation Michal Pluhacek Faculty of Applied Informatics Tomas Bata University in Zlin Zlin, Czech Republic pluhacek@utb.cz Adam Viktorin Faculty of Applied Informatics Tomas Bata University in Zlin Zlin, Czech Republic aviktorin@utb.cz Tomas Kadavy Faculty of Applied Informatics Tomas Bata University in Zlin Zlin, Czech Republic kadavy@utb.cz Anezka Kazikova Faculty of Applied Informatics Tomas Bata University in Zlin Zlin, Czech Republic kazikova@utb.cz
utb.fulltext.dates -
utb.fulltext.sponsorship This work was supported by the Internal Grant Agency of Tomas Bata University under the Projects no. IGA/CebiaTech/2021/001, and further by the resources of A.I.Lab at the Faculty of Applied Informatics, Tomas Bata University in Zlin (ailab.fai.utb.cz).
utb.wos.affiliation [Pluhacek, Michal; Viktorin, Adam; Kadavy, Tomas; Kazikova, Anezka] Tomas Bata Univ Zlin, Fac Appl Informat, Zlin, Czech Republic
utb.scopus.affiliation Tomas Bata University in Zlin, Faculty of Applied Informatics, Zlin, Czech Republic
utb.fulltext.projects IGA/CebiaTech/2021/001
utb.fulltext.faculty Faculty of Applied Informatics
utb.fulltext.ou -
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