Challenging the Limits of Binarization: A New Scheme Selection Policy Using Reinforcement Learning Techniques for Binary Combinatorial Problem Solving

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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Bibliographic Details
Main Authors: Marcelo Becerra-Rozas, Broderick Crawford, Ricardo Soto, El-Ghazali Talbi, Jose M. Gómez-Pulido
Format: Article
Language:English
Published: MDPI AG 2024-02-01
Series:Biomimetics
Subjects:
binarization
schemes selection
binary optimization
metaheuristics
reinforcement learning
policy
Online Access:https://www.mdpi.com/2313-7673/9/2/89

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https://www.mdpi.com/2313-7673/9/2/89

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