Adaptability and Efficiency in Population Management: A multi-population CMA-ES Strategy for High-Dimensional Optimization
Bernardo Morales-Castañeda, Erick Rodríguez-Esparza, Diego A. Oliva, Mario A. Navarro, Itzel Aranguren, Ángel Casas-Ordaz, Luis A. Beltran, Saul Zapotecas Martinez · Procedia Computer Science · 2024
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.