Intelligent decision-making for binary coverage: Unveiling the potential of the multi-armed bandit selector

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dc.contributor.authorBecerra-Rozas, Marcelo
dc.contributor.authorLemus-Romani, Jose
dc.contributor.authorCrawford, Broderick
dc.contributor.authorSoto, Ricardo
dc.contributor.authorTalbi, El-Ghazali
dc.date.accessioned2025-01-20T16:16:58Z
dc.date.available2025-01-20T16:16:58Z
dc.date.issued2024
dc.description.abstractIn this article, we propose the integration of a novel reinforcement learning technique into our generic and unified framework. This framework enables any continuous metaheuristic to operate in binary optimization, with the technique in question known as the Multi -Armed Bandit. Population -based metaheuristics comprise multiple individuals that cooperatively and globally explore the search space using their limited individual capabilities. Our framework allows these population -based metaheuristics to continue leveraging their original movements, designed for continuous optimization, once they are binary encoded. The generality of the framework has facilitated the instantiation of popular algorithms from the optimization, machine learning, and evolutionary computing communities. Furthermore, it permits the design of new and innovative optimization instances using various component strategies, reflecting the framework's modularity. The results comparing two statistical techniques and three hybridizations coming from Machine Learning, have shown to obtain a better performance with the metahuristics in Grey Wolf Optimizer and Whale Optimization Algorithm.
dc.fuente.origenWOS
dc.identifier.doi10.1016/j.eswa.2024.124112
dc.identifier.eissn1873-6793
dc.identifier.issn0957-4174
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2024.124112
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/90573
dc.identifier.wosidWOS:001234821400001
dc.language.isoen
dc.revistaExpert systems with applications
dc.rightsacceso restringido
dc.subjectBinarization schemes selection
dc.subjectBinary optimization
dc.subjectMetaheuristics
dc.subjectReinforcement learning
dc.subjectDecision making
dc.titleIntelligent decision-making for binary coverage: Unveiling the potential of the multi-armed bandit selector
dc.typeartículo
dc.volumen251
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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