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Clustering analysis of the population in Db_SHADE algorithm

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dc.title Clustering analysis of the population in Db_SHADE algorithm en
dc.contributor.author Viktorin, Adam
dc.contributor.author Šenkeřík, Roman
dc.contributor.author Pluháček, Michal
dc.contributor.author Kadavý, Tomáš
dc.relation.ispartof Mendel
dc.identifier.issn 1803-3814 OCLC, Ulrich, Sherpa/RoMEO, JCR
dc.date.issued 2018
utb.relation.volume 24
utb.relation.issue 1
dc.citation.spage 9
dc.citation.epage 16
dc.type article
dc.language.iso en
dc.publisher Brno University of Technology
dc.identifier.doi 10.13164/mendel.2018.1.009
dc.relation.uri https://mendel-journal.org/index.php/mendel/article/view/13
dc.subject DBSCAN en
dc.subject Differential evolution en
dc.subject Distance based parameter adaptation en
dc.subject SHADE en
dc.description.abstract This paper provides an analysis of the population clustering in a novel Success-History based Adaptive Differential Evolution algorithm with Distance based adaptation (Db_SHADE) in order to analyze the exploration and exploitation abilities of the algorithm. The comparison with the original SHADE algorithm is performed on the CEC2015 benchmark set in two dimensional settings (10D and 30D). The clustering analysis helps to answer the question about prolonged exploration phase of the Db_SHADE algorithm. Possible future research directions are drawn in the discussion and conclusion. © 2018, Brno University of Technology. All rights reserved. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1009089
utb.identifier.scopus 2-s2.0-85072046299
utb.source j-scopus
dc.date.accessioned 2019-09-19T07:56:15Z
dc.date.available 2019-09-19T07:56:15Z
dc.rights Attribution-NonCommercial-ShareAlike 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.access openAccess
utb.ou CEBIA-Tech
utb.contributor.internalauthor Viktorin, Adam
utb.contributor.internalauthor Šenkeřík, Roman
utb.contributor.internalauthor Pluháček, Michal
utb.contributor.internalauthor Kadavý, Tomáš
utb.fulltext.sponsorship This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme Project no. LO1303 (MSMT-7778/2014), further by the European Regional Development Fund under the Project CEBIA-Tech no. CZ.1.05/2.1.00/03.0089 and by Internal Grant Agency of Tomas Bata University under the Projects no. IGA/CebiaTech/2018/003. This work is also based upon support by COST (European Cooperation in Science & Technology) under Action CA15140, Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO), and Action IC1406, High-Performance Modelling and Simulation for Big Data Applications (cHiPSet). The work was further supported by 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, Faculty of Applied Informatics, T. G. Masaryka 5555, Zlin, 760 01, Czech Republic
utb.fulltext.projects LO1303
utb.fulltext.projects MSMT-7778/2014
utb.fulltext.projects CZ.1.05/2.1.00/03.0089
utb.fulltext.projects IGA/CebiaTech/2018/003
utb.fulltext.projects CA15140
utb.fulltext.projects IC1406
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