Swarm-Inspired Computing to Solve Binary Optimization Problems: A Backward Q-Learning Binarization Scheme Selector

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dc.contributor.authorBecerra-Rozas, Marcelo
dc.contributor.authorLemus-Romani, Jose
dc.contributor.authorCisternas-Caneo, Felipe
dc.contributor.authorCrawford, Broderick
dc.contributor.authorSoto, Ricardo
dc.contributor.authorGarcia, Jose
dc.date.accessioned2025-01-20T21:01:00Z
dc.date.available2025-01-20T21:01:00Z
dc.date.issued2022
dc.description.abstractIn recent years, continuous metaheuristics have been a trend in solving binary-based combinatorial problems due to their good results. However, to use this type of metaheuristics, it is necessary to adapt them to work in binary environments, and in general, this adaptation is not trivial. The method proposed in this work evaluates the use of reinforcement learning techniques in the binarization process. Specifically, the backward Q-learning technique is explored to choose binarization schemes intelligently. This allows any continuous metaheuristic to be adapted to binary environments. The illustrated results are competitive, thus providing a novel option to address different complex problems in the industry.
dc.fuente.origenWOS
dc.identifier.doi10.3390/math10244776
dc.identifier.eissn2227-7390
dc.identifier.urihttps://doi.org/10.3390/math10244776
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/92801
dc.identifier.wosidWOS:000904171600001
dc.issue.numero24
dc.language.isoen
dc.revistaMathematics
dc.rightsacceso restringido
dc.subjectcombinatorial problems
dc.subjectmetaheuristics
dc.subjectbinarization scheme
dc.subjectbackward Q-learning
dc.subjectmachine learning
dc.titleSwarm-Inspired Computing to Solve Binary Optimization Problems: A Backward Q-Learning Binarization Scheme Selector
dc.typeartículo
dc.volumen10
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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