Adaptability and efficiency in population management: a multi-population CMA-ES strategy for high-dimensional optimization

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morales_adaptability_2024.pdf (2.2 MB)
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2024
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Elsevier B.V.
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Resumen
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.
Palabras clave
CMA-ES Function evaluation
Covariance matrix
Evolutionary algorithms
Population
Descripción
Ponencia presentada en la 28th International Conference on Knowledge Based and Intelligent information and Engineering Systems (KES 2024), celebrada en Sevilla, entre el 11 y el 13 de septiembre de 2024
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Morales-Castañeda, B., Rodríguez-Esparza, E., Oliva, D., Navarro, M. A., Aranguren, I., Casas-Ordaz, A., Beltran, L. A., & Zapotecas-Martínez, S. (2024). Adaptability and efficiency in population management: a multi-population CMA-ES strategy for high-dimensional optimization. Procedia Computer Science, 246(C), 1389-1398. https://doi.org/10.1016/J.PROCS.2024.09.579
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http://hdl.handle.net/20.500.14454/2922
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