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dc.contributor.authorSilveira, Lucas Ângelo da-
dc.contributor.authorLima, Thaynara Arielly de-
dc.contributor.authorBarros, Jessé Barreto de Barros-
dc.contributor.authorSoncco-Álvarez, José Luis-
dc.contributor.authorLlanos Quintero, Carlos Humberto-
dc.contributor.authorAyala-Rincón, Mauricio-
dc.date.accessioned2024-09-17T16:57:59Z-
dc.date.available2024-09-17T16:57:59Z-
dc.date.issued2023-
dc.identifier.citationSILVEIRA, Lucas A. de et al. On the behavior of parallel island models. Applied Soft Computing, [S. l.], v. 148, 110880, nov. 2023. DOI: https://doi.org/10.1016/j.asoc.2023.110880.pt_BR
dc.identifier.urihttp://repositorio.unb.br/handle/10482/50367-
dc.language.isoengpt_BR
dc.publisherElsevier B. V.pt_BR
dc.rightsAcesso Restritopt_BR
dc.titleOn the behavior of parallel island modelspt_BR
dc.typeArtigopt_BR
dc.subject.keywordAlgoritmos genéticospt_BR
dc.subject.keywordModelos de ilhas paralelaspt_BR
dc.identifier.doihttps://doi.org/10.1016/j.asoc.2023.110880pt_BR
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/abs/pii/S1568494623008980?via%3Dihubpt_BR
dc.description.abstract1Parallel island models (PIMs) are used to enhance the performance of evolutionary algorithms (EAs). In PIMs, each island executes an EA for evolving its local population, and periodically individuals migrate to neighborhoods synchronously or asynchronously. Neighborhoods are organized through a topology of communication, and migration policies guide individual exchange. This work explores migration policies over different communication topologies in synchronous and asynchronous PIMs to improve the speed-up and accuracy of genetic algorithms (GAs). The aim is to explain such models’ adequacy from a general perspective, attempting to answer questions such as the best way to implement GAs in PIMs. To reach this goal, the quality of the solutions and the running time provided by proposed PIMs are evaluated over four -hard problems of different combinatorial nature: reversal and translocation evolutionary distance, task mapping and scheduling, and -Queens. The experiments show that tuning the parameters of the breeding cycle and migration policies is vital to guarantee that all proposed PIMs reach good speed-ups and more accurate solutions than the sequential GA. In addition, experiments ratify that synchronous models provide the best solutions, while asynchronous models deliver the best speed-ups. Finally, the results show that no model provides either better speed-up or accuracy in general since, as for sequential models, the nature of the problem define which would be the best-adapted PIM.pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-8107-9659pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-0852-9086pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0001-9435-2688pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-3544-5777pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-0115-4461pt_BR
dc.contributor.affiliationUniversidade de Brasília, Department of Computer Sciencept_BR
dc.contributor.affiliationUniversidade Federal de Goiás, Department of Mathematicspt_BR
dc.contributor.affiliationUniversidade de Brasília, Department of Mechanical Engineeringpt_BR
dc.contributor.affiliationUniversidad Nacional de San Antonio Abad del Cusco, Department of Informatics, Perupt_BR
dc.contributor.affiliationUniversidade de Brasília, Department of Mechanical Engineeringpt_BR
dc.description.unidadeInstituto de Ciências Exatas (IE)pt_BR
dc.description.unidadeDepartamento de Ciência da Computação (IE CIC)pt_BR
dc.description.unidadeFaculdade de Tecnologia (FT)pt_BR
dc.description.unidadeDepartamento de Engenharia Mecânica (FT ENM)pt_BR
dc.description.ppgPrograma de Pós-Graduação em Informáticapt_BR
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