Intelligent decision-making for binary coverage: Unveiling the potential of the multi-armed bandit selector.
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| Title: | Intelligent decision-making for binary coverage: Unveiling the potential of the multi-armed bandit selector. |
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| Authors: | Becerra-Rozas, Marcelo1 (AUTHOR) marcelo.becerra.r@mail.pucv.cl, Lemus-Romani, José2 (AUTHOR) jose.lemus@uc.cl, Crawford, Broderick1 (AUTHOR) broderick.crawford@pucv.cl, Soto, Ricardo1 (AUTHOR) ricardo.soto@pucv.cl, Talbi, El-Ghazali3 (AUTHOR) el-ghazali.talbi@univ-lille.fr |
| Source: | Expert Systems with Applications. Oct2024, Vol. 251, pN.PAG-N.PAG. 1p. |
| Subjects: | Metaheuristic algorithms, Grey Wolf Optimizer algorithm, Evolutionary algorithms, Reinforcement learning, Machine learning, Decision making |
| Abstract: | In 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 177514340 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Intelligent decision-making for binary coverage: Unveiling the potential of the multi-armed bandit selector. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Becerra-Rozas%2C+Marcelo%22">Becerra-Rozas, Marcelo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> marcelo.becerra.r@mail.pucv.cl</i><br /><searchLink fieldCode="AR" term="%22Lemus-Romani%2C+José%22">Lemus-Romani, José</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> jose.lemus@uc.cl</i><br /><searchLink fieldCode="AR" term="%22Crawford%2C+Broderick%22">Crawford, Broderick</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> broderick.crawford@pucv.cl</i><br /><searchLink fieldCode="AR" term="%22Soto%2C+Ricardo%22">Soto, Ricardo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ricardo.soto@pucv.cl</i><br /><searchLink fieldCode="AR" term="%22Talbi%2C+El-Ghazali%22">Talbi, El-Ghazali</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> el-ghazali.talbi@univ-lille.fr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Expert+Systems+with+Applications%22">Expert Systems with Applications</searchLink>. Oct2024, Vol. 251, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Grey+Wolf+Optimizer+algorithm%22">Grey Wolf Optimizer algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Evolutionary+algorithms%22">Evolutionary algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.eswa.2024.124112 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Metaheuristic algorithms Type: general – SubjectFull: Grey Wolf Optimizer algorithm Type: general – SubjectFull: Evolutionary algorithms Type: general – SubjectFull: Reinforcement learning Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Decision making Type: general Titles: – TitleFull: Intelligent decision-making for binary coverage: Unveiling the potential of the multi-armed bandit selector. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Becerra-Rozas, Marcelo – PersonEntity: Name: NameFull: Lemus-Romani, José – PersonEntity: Name: NameFull: Crawford, Broderick – PersonEntity: Name: NameFull: Soto, Ricardo – PersonEntity: Name: NameFull: Talbi, El-Ghazali IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 09574174 Numbering: – Type: volume Value: 251 Titles: – TitleFull: Expert Systems with Applications Type: main |
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