Challenging the Limits of Binarization: A New Scheme Selection Policy Using Reinforcement Learning Techniques for Binary Combinatorial Problem Solving
In this study, we introduce an innovative policy in the field of reinforcement learning, specifically designed as an action selection mechanism, and applied herein as a selector for binarization schemes. These schemes enable continuous metaheuristics to be applied to binary problems, thereby paving...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/9/2/89 |
| _version_ | 1797298846588469248 |
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| author | Marcelo Becerra-Rozas Broderick Crawford Ricardo Soto El-Ghazali Talbi Jose M. Gómez-Pulido |
| author_facet | Marcelo Becerra-Rozas Broderick Crawford Ricardo Soto El-Ghazali Talbi Jose M. Gómez-Pulido |
| author_sort | Marcelo Becerra-Rozas |
| collection | DOAJ |
| description | In this study, we introduce an innovative policy in the field of reinforcement learning, specifically designed as an action selection mechanism, and applied herein as a selector for binarization schemes. These schemes enable continuous metaheuristics to be applied to binary problems, thereby paving new paths in combinatorial optimization. To evaluate its efficacy, we implemented this policy within our BSS framework, which integrates a variety of reinforcement learning and metaheuristic techniques. Upon resolving 45 instances of the Set Covering Problem, our results demonstrate that reinforcement learning can play a crucial role in enhancing the binarization techniques employed. This policy not only significantly outperformed traditional methods in terms of precision and efficiency, but also proved to be extensible and adaptable to other techniques and similar problems. The approach proposed in this article is capable of significantly surpassing traditional methods in precision and efficiency, which could have important implications for a wide range of real-world applications. This study underscores the philosophy behind our approach: utilizing reinforcement learning not as an end in itself, but as a powerful tool for solving binary combinatorial problems, emphasizing its practical applicability and potential to transform the way we address complex challenges across various fields. |
| first_indexed | 2024-03-07T22:40:59Z |
| format | Article |
| id | doaj.art-b8cc776203284f8386f6e63ce712eb9d |
| institution | Directory Open Access Journal |
| issn | 2313-7673 |
| language | English |
| last_indexed | 2024-03-07T22:40:59Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj.art-b8cc776203284f8386f6e63ce712eb9d2024-02-23T15:09:05ZengMDPI AGBiomimetics2313-76732024-02-01928910.3390/biomimetics9020089Challenging the Limits of Binarization: A New Scheme Selection Policy Using Reinforcement Learning Techniques for Binary Combinatorial Problem SolvingMarcelo Becerra-Rozas0Broderick Crawford1Ricardo Soto2El-Ghazali Talbi3Jose M. Gómez-Pulido4Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, ChileEscuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, ChileEscuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, ChileCNRS UMR 9189, Centre de Recherche en Informatique Signal et Automatique de Lille (CRIStAL), University of Lille, F-59000 Lille, FranceHealth Computing and Intelligent Systems Research Group (HCIS), Department of Computer Science, University of Alcalá, 28805 Alcalá de Henares, SpainIn this study, we introduce an innovative policy in the field of reinforcement learning, specifically designed as an action selection mechanism, and applied herein as a selector for binarization schemes. These schemes enable continuous metaheuristics to be applied to binary problems, thereby paving new paths in combinatorial optimization. To evaluate its efficacy, we implemented this policy within our BSS framework, which integrates a variety of reinforcement learning and metaheuristic techniques. Upon resolving 45 instances of the Set Covering Problem, our results demonstrate that reinforcement learning can play a crucial role in enhancing the binarization techniques employed. This policy not only significantly outperformed traditional methods in terms of precision and efficiency, but also proved to be extensible and adaptable to other techniques and similar problems. The approach proposed in this article is capable of significantly surpassing traditional methods in precision and efficiency, which could have important implications for a wide range of real-world applications. This study underscores the philosophy behind our approach: utilizing reinforcement learning not as an end in itself, but as a powerful tool for solving binary combinatorial problems, emphasizing its practical applicability and potential to transform the way we address complex challenges across various fields.https://www.mdpi.com/2313-7673/9/2/89binarizationschemes selectionbinary optimizationmetaheuristicsreinforcement learningpolicy |
| spellingShingle | Marcelo Becerra-Rozas Broderick Crawford Ricardo Soto El-Ghazali Talbi Jose M. Gómez-Pulido Challenging the Limits of Binarization: A New Scheme Selection Policy Using Reinforcement Learning Techniques for Binary Combinatorial Problem Solving Biomimetics binarization schemes selection binary optimization metaheuristics reinforcement learning policy |
| title | Challenging the Limits of Binarization: A New Scheme Selection Policy Using Reinforcement Learning Techniques for Binary Combinatorial Problem Solving |
| title_full | Challenging the Limits of Binarization: A New Scheme Selection Policy Using Reinforcement Learning Techniques for Binary Combinatorial Problem Solving |
| title_fullStr | Challenging the Limits of Binarization: A New Scheme Selection Policy Using Reinforcement Learning Techniques for Binary Combinatorial Problem Solving |
| title_full_unstemmed | Challenging the Limits of Binarization: A New Scheme Selection Policy Using Reinforcement Learning Techniques for Binary Combinatorial Problem Solving |
| title_short | Challenging the Limits of Binarization: A New Scheme Selection Policy Using Reinforcement Learning Techniques for Binary Combinatorial Problem Solving |
| title_sort | challenging the limits of binarization a new scheme selection policy using reinforcement learning techniques for binary combinatorial problem solving |
| topic | binarization schemes selection binary optimization metaheuristics reinforcement learning policy |
| url | https://www.mdpi.com/2313-7673/9/2/89 |
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