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Please use this identifier to cite or link to this item: https://research.matf.bg.ac.rs/handle/123456789/3061
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dc.contributor.authorSavić, Aleksandaren_US
dc.contributor.authorTošić, Dušanen_US
dc.contributor.authorMarić, Miroslaven_US
dc.contributor.authorKratica, Jozefen_US
dc.date.accessioned2026-01-13T15:05:09Z-
dc.date.available2026-01-13T15:05:09Z-
dc.date.issued2008-
dc.identifier.urihttps://research.matf.bg.ac.rs/handle/123456789/3061-
dc.description.abstractIn this paper a genetic algorithm (GA) for the task assignment problem (TAP) is considered.An integer representation with standard genetic operators is used. Computational results are presented for instances from the literature, and compared to optimal solutions obtained by the CPLEX solver. It can be seen that the proposed GA approach reaches 17 of 20 optimal solutions. The GA solutions are obtained in a quite a short amount of computational time.en_US
dc.language.isoenen_US
dc.publisherSofia : Institute of Mathematics and Informatics at the Bulgarian Academy of Sciencesen_US
dc.relation.ispartofSerdica Journal of Computingen_US
dc.subjectEvolutionary Approachen_US
dc.subjectGenetic Algorithmsen_US
dc.subjectAssignment Problemsen_US
dc.subjectMultiprocessor Systemsen_US
dc.subjectCombinatorial Optimizationen_US
dc.titleGenetic algorithm approach for solving the task assignment problemen_US
dc.typeArticleen_US
dc.identifier.urlhttp://serdica-comp.math.bas.bg/index.php/serdicajcomputing/article/view/53-
dc.relation.issn1312-6555en_US
dc.description.rankM20/M50en_US
dc.relation.firstpage267en_US
dc.relation.lastpage276en_US
dc.relation.volume2en_US
dc.relation.issue3en_US
item.openairetypeArticle-
item.cerifentitytypePublications-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.orcid0009-0003-8568-4260-
crisitem.author.orcid0000-0001-7446-0577-
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