Reinforcement Learning Based Whale Optimizer

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dc.catalogadorjlo
dc.contributor.authorBecerra Rozas, Marcelo
dc.contributor.authorLemus Romani, José Isaac
dc.contributor.authorCrawford, Broderick
dc.contributor.authorSoto, Ricardo
dc.contributor.authorCisternas Caneo, Felipe
dc.contributor.authorEmbry, Andres Trujillo
dc.contributor.authorMolina, Maximo Arnao
dc.contributor.authorTapia, Diego
dc.contributor.authorCastillo, Mauricio
dc.contributor.authorMisra, Sanjay
dc.contributor.authorRubio, Jose Miguel
dc.date.accessioned2026-01-09T16:11:43Z
dc.date.available2026-01-09T16:11:43Z
dc.date.issued2021
dc.description.abstractThis work proposes a Reinforcement Learning based optimizer integrating SARSA and Whale Optimization Algorithm. SARSA determines the binarization operator required during the metaheuristic process. The hybrid instance is applied to solve benchmarks of the Set Covering Problem and it is compared with a Q-learning version, showing good results in terms of fitness, specifically, SARSA beats its Q-Learning version in 44 out of 45 instances evaluated. It is worth mentioning that the only instance where it does not win is a tie. Finally, thanks to graphs presented in our results analysis we can observe that not only does it obtain good results, it also obtains a correct exploration and exploitation balance as presented in the referenced literature.
dc.description.funderNational Agency for Research and Development (ANID)/Scholarship Program/DOCTORADO NACIONAL
dc.format.extent15
dc.fuente.origenWOS
dc.identifier.doi10.1007/978-3-030-87013-3_16
dc.identifier.eisbn978-3-030-87013-3
dc.identifier.eissn1611-3349
dc.identifier.isbn978-3-030-87012-6
dc.identifier.issn0302-9743
dc.identifier.scopusidSCOPUS_ID:85115713527
dc.identifier.urihttps://doi.org/10.1007/978-3-030-87013-3_16
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/107622
dc.identifier.wosidWOS:000722395800016
dc.information.autorucEscuela de Construcción Civil; Lemus Romani, José Isaac; 0000-0001-5379-0315; 1223124
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final219
dc.pagina.inicio205
dc.publisherSpringer International
dc.relation.isformatof21st International Conference on Computational Science and Its Applications (ICCSA), SEP 13-16, 2021, Cagliari, ITALY
dc.rightsacceso restringido
dc.subjectMetaheuristic
dc.subjectSARSA
dc.subjectQ-Learning
dc.subjectSwarm intelligence
dc.subjectWhale optimization algorithm
dc.subjectCombinatorial optimization
dc.subject.ddc620
dc.titleReinforcement Learning Based Whale Optimizer
dc.typecomunicación de congreso
dc.volumen12957
sipa.codpersvinculados1223124
sipa.trazabilidadWOS;18-03-2022
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