Browsing by Author "Castillo, Mauricio"

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    A Novel Learning-Based Binarization Scheme Selector for Swarm Algorithms Solving Combinatorial Problems
    (2021) Lemus-Romani, Jose; Becerra-Rozas, Marcelo; Crawford, Broderick; Soto, Ricardo; Cisternas-Caneo, Felipe; Vega, Emanuel; Castillo, Mauricio; Tapia, Diego; Astorga, Gino; Palma, Wenceslao; Castro, Carlos; Garcia, Jose
    Currently, industry is undergoing an exponential increase in binary-based combinatorial problems. In this regard, metaheuristics have been a common trend in the field in order to design approaches to successfully solve them. Thus, a well-known strategy includes the employment of continuous swarm-based algorithms transformed to perform in binary environments. In this work, we propose a hybrid approach that contains discrete smartly adapted population-based strategies to efficiently tackle binary-based problems. The proposed approach employs a reinforcement learning technique, known as SARSA (State-Action-Reward-State-Action), in order to utilize knowledge based on the run time. In order to test the viability and competitiveness of our proposal, we compare discrete state-of-the-art algorithms smartly assisted by SARSA. Finally, we illustrate interesting results where the proposed hybrid outperforms other approaches, thus, providing a novel option to tackle these types of problems in industry.