Examinando por Autor "Navarro, Mario A."

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    Adaptability and efficiency in population management: a multi-population CMA-ES strategy for high-dimensional optimization
    (Elsevier B.V., 2024) Morales Castañeda, Bernardo; Rodríguez Esparza, Erick; Oliva, Diego; Navarro, Mario A.; Aranguren, Itzel; Casas Ordaz, Ángel; Beltran, Luis A.; Zapotecas Martínez, Saúl
    In the context of evolutionary algorithms, having the ability to adapt to any search space within an optimization problem is an essential task. Appropriately adapting the population can lead to better solutions and more efficient use of function call resources. This article presents a renewed approach to population management inspired by modifying the well-known Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. The proposed strategy aims to improve the algorithm's population adaptability to the search space and optimize function evaluations. Statistically evaluated experimental test outcomes demonstrate significantly better performance on high-dimensional problems in comparison to the original CMA-ES and seven other known evolutionary algorithms in the literature.
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    Ítem
    Handling the balance of operators in evolutionary algorithms through a weighted Hill Climbing approach
    (Elsevier B.V., 2024-06-21) Rodríguez Esparza, Erick; Morales Castañeda, Bernardo; Casas Ordaz, Ángel; Oliva, Diego; Navarro, Mario A.; Valdivia, Arturo; Houssein, Essam H.
    Evolutionary Algorithms (EAs) are a well-known domain within Artificial Intelligence. EAs have demonstrated their ability to tackle intricate optimization problems using evolutionary theory principles. However, balancing the dual exploration and exploitation processes remains a crucial concern. This paper introduces the Balanced Hill Climbing Weight Algorithm with Diversity (BHWEAD), an innovative approach that combines elements from classic Genetic Algorithm and Differential Evolution. BHWEAD uniquely employs the Hill Climbing local search to guide the influence of its operators, ensuring an optimal interplay between exploration and exploitation. Additionally, it incorporates a diversity control mechanism, resetting specific solutions to prevent premature convergence to suboptimal solutions. The main contribution of the BHWEAD is the mechanism that permits the balance of the exploration and exploitation stages; also, the incorporation of Hill Climbing permits a proper balance of the influence of the operators. Notice that the proposal can escape from suboptimal solutions using a diversity-based strategy. Tested against the CEC2017 benchmark functions in both 50 and 100 dimensions, BHWEAD outperformed 12 notable EAs, underscoring its potential for high-dimensional optimization problems. Besides, the proposed BHWEAD has also been tested over seven engineering problems, and the comparisons include some memetic algorithms., The paper provides additional insights into the algorithm's design, conducts a comparative analysis, and identifies potential areas for improvement.