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Impact of boundary control methods on bound-constrained optimization benchmarking

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dc.title Impact of boundary control methods on bound-constrained optimization benchmarking en
dc.contributor.author Kadavý, Tomáš
dc.contributor.author Viktorin, Adam
dc.contributor.author Kazíková, Anežka
dc.contributor.author Pluháček, Michal
dc.contributor.author Šenkeřík, Roman
dc.relation.ispartof GECCO 2023 Companion - Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion
dc.identifier.isbn 979-840070120-7
dc.date.issued 2023
dc.citation.spage 25
dc.citation.epage 26
dc.event.title 2023 Genetic and Evolutionary Computation Conference Companion, GECCO 2023 Companion
dc.event.location Lisbon
utb.event.state-en Portugal
utb.event.state-cs Portugalsko
dc.event.sdate 2023-07-15
dc.event.edate 2023-07-19
dc.type conferenceObject
dc.language.iso en
dc.publisher Association for Computing Machinery, Inc
dc.identifier.doi 10.1145/3583133.3595849
dc.relation.uri https://dl.acm.org/doi/10.1145/3583133.3595849
dc.relation.uri https://dl.acm.org/doi/pdf/10.1145/3583133.3595849
dc.subject evolutionary computation en
dc.subject computational intelligence en
dc.subject performance evaluation en
dc.subject benchmark testing en
dc.subject boundary control method en
dc.description.abstract Despite initial indifference towards boundary control methods (BCM) in the context of metaheuristic algorithm design, benchmarking, and execution, our research demonstrates their critical importance. This study investigates how the choice of a particular BCM can profoundly influence the performance of competitive algorithms. We analyzed the top three algorithms from the 2017 and 2020 IEEE CEC competitions, posing the following question: Could a change in BCM usage alter an algorithm’s overall performance and, consequently, its ranking among competitors? Our findings reveal that paying attention to BCMs can lead to significant improvements. The experiments revealed that BCM selection can significantly impact an algorithm’s performance and, in some instances, its competition rank. However, most authors omitted to mention the implemented BCM, resulting in poor reproducibility and deviating from recommended benchmarking practices for metaheuristic algorithms. The conclusion is that the BCM should be considered another vital metaheuristics input variable for unambiguous reproducibility of results in benchmarking and for a better understanding of population dynamics. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1011660
utb.identifier.obdid 43884590
utb.identifier.scopus 2-s2.0-85169062127
utb.identifier.wok 001117972600013
utb.source d-scopus
dc.date.accessioned 2023-12-05T11:36:25Z
dc.date.available 2023-12-05T11:36:25Z
dc.description.sponsorship Tomas Bata University in Zlin, TBU, (IGA/CebiaTech/2023/004)
dc.description.sponsorship Internal Grant Agency of the Tomas Bata University in Zlin [IGA/CebiaTech/2023/004]
utb.contributor.internalauthor Kadavý, Tomáš
utb.contributor.internalauthor Viktorin, Adam
utb.contributor.internalauthor Kazíková, Anežka
utb.contributor.internalauthor Pluháček, Michal
utb.contributor.internalauthor Šenkeřík, Roman
utb.fulltext.sponsorship We acknowledge the support of the Internal Grant Agency of the Tomas Bata University in Zlin, project No. IGA/CebiaTech/2023/004.
utb.wos.affiliation [Kadavy, Tomas; Viktorin, Adam; Kazikova, Anezka; Pluhacek, Michal; Senkerik, Roman] Tomas Bata Univ Zlin, Zlin, Czech Republic
utb.scopus.affiliation Tomas Bata University in Zlin, Zlin, Czech Republic
utb.fulltext.projects IGA/CebiaTech/2023/004
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