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Explaining SOMA: The relation of stochastic perturbation to population diversity and parameter space coverage

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dc.title Explaining SOMA: The relation of stochastic perturbation to population diversity and parameter space coverage en
dc.contributor.author Pluháček, Michal
dc.contributor.author Kazíková, Anežka
dc.contributor.author Kadavý, Tomáš
dc.contributor.author Viktorin, Adam
dc.contributor.author Šenkeřík, Roman
dc.relation.ispartof GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion
dc.identifier.isbn 978-1-4503-8351-6
dc.date.issued 2021
dc.citation.spage 1944
dc.citation.epage 1952
dc.event.title 2021 Genetic and Evolutionary Computation Conference, GECCO 2021
dc.event.location online
dc.event.sdate 2021-07-10
dc.event.edate 2021-07-14
dc.type conferenceObject
dc.language.iso en
dc.publisher Association for Computing Machinery, Inc
dc.identifier.doi 10.1145/3449726.3463211
dc.relation.uri https://dl.acm.org/doi/10.1145/3449726.3463211
dc.subject diversity en
dc.subject parameter space coverage en
dc.subject perturbation en
dc.subject self-organizing migrating algorithm en
dc.subject SOMA en
dc.description.abstract The Self-Organizing Migrating Algorithm (SOMA) is enjoying a renewed interest of the research community, following recent achievements in various application areas and renowned performance competitions. In this paper, we focus on the importance and effect of the perturbation operator in SOMA as the perturbation is one of the fundamental inner principles of SOMA. In this in-depth study, we present data, visualizations, and analysis of the effect of the perturbation on the population, its diversity and average movement patterns. We provide evidence that there is a direct relation between the perturbation intensity (set by control parameter prt) and the rate of diversity loss. The perturbation setting further affects the exploratory ability of the algorithm, as is demonstrated here by analysing the parameter space coverage of the population. We aim to provide insight and explanation of the impact of perturbation in SOMA for future researchers and practitioners. © 2021 ACM. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1010469
utb.identifier.obdid 43883338
utb.identifier.scopus 2-s2.0-85111059980
utb.source d-scopus
dc.date.accessioned 2021-08-17T07:36:50Z
dc.date.available 2021-08-17T07:36:50Z
dc.description.sponsorship IGA/CebiaTech/2021/001
utb.contributor.internalauthor Pluháček, Michal
utb.contributor.internalauthor Kazíková, Anežka
utb.contributor.internalauthor Kadavý, Tomáš
utb.contributor.internalauthor Viktorin, Adam
utb.contributor.internalauthor Šenkeřík, Roman
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.scopus.affiliation Tomas Bata University in Zlin, Zlin, Czech Republic
utb.fulltext.projects IGA/CebiaTech/2021/001
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