Broderick Crawford

Apr 20, 2024 | mdpi.com | Rodrigo Olivares |Camilo Ravelo |Ricardo Soto |Broderick Crawford

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Mar 26, 2024 | preprints.org | Rodrigo Olivares |Ricardo Soto |Broderick Crawford |Camilo Ravelo

Preprint Article Version 1 This version is not peer-reviewed Version 1 : Received: 25 March 2024 / Approved: 26 March 2024 / Online: 26 March 2024 (07:27:45 CET) Olivares, R.; Ravelo, C.; Soto, R.; Crawford, B. Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection. Preprints 2024, 2024031533. https://doi.org/10.20944/preprints202403.1533.v1 Olivares, R.; Ravelo, C.; Soto, R.; Crawford, B.

Jun 28, 2023 | mdpi.com | Rodrigo Olivares |Ricardo Soto |Broderick Crawford |Pablo Olivares

6. DiscussionAll algorithms were run 30 times in the testing phase for each instance. Results were recorded, distinguishing each method to be further compared (native PSO or NPSO, classic Q–Learning or CQL, modified Q–Learning or MQL, and single state Q–Learning or SSQL). Table 2 summarizes how many known optimums were found for each version, and in the case of the instances with unknown optimums, how many of these could reach the best solution found in a limited testing time.

Jun 14, 2023 | mdpi.com | Jose Garcia |José García |Broderick Crawford |Ricardo Soto |Andres Leiva-Araos

1. IntroductionOver the past few years, metaheuristic methods have emerged as powerful tools in addressing complex problems, particularly those pertaining to the realm of combinatorial challenges. A wide array of applications across various fields, including biology [1,2], logistics [3], civil engineering [4,5], machine learning [6], and many more, serve as compelling evidence of their effectiveness in problem-solving.