Abstract
In today’s business environment, organizations face complex challenges that require efficient and effective solutions. Process optimization and decision-making in line with the decision-maker’s preferences are crucial for success and competitive advantage. Often, business problems involve optimizing multiple conflicting objectives and imprecise preferences. Coevolutionary algorithms have gained popularity as effective tools for solving multi-objective problems. These techniques allow the simultaneous evolution of multiple solutions through the interaction and joint evolution of populations. They encourage cooperative improvement of solutions, promoting diversity and the discovery of optima in complex problems. Parameter tuning is critical in these algorithms as it directly influences their performance. Parameters determine their behavior and exploration, and proper tuning enhances the algorithm’s ability to avoid local optima and search for global solutions.
Claudia Gómez-Santillán, Nelson Rangel-Valdez, Marco Aguirre-Lam, Lucila Morales-Rodriguez and Fausto Balderas-Jaramillo—These authors contributed equally to this work.
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Rosas-Solórzano, L., Gómez-Santillán, C., Rangel-Valdez, N., Aguirre-Lam, M., Morales-Rodriguez, L., Balderas-Jaramillo, F. (2024). Experimental Design Method to Finetune Cooperative Coevolutionary Algorithms Solving Multiobjective Problems. In: Castillo, O., Melin, P. (eds) New Horizons for Fuzzy Logic, Neural Networks and Metaheuristics. Studies in Computational Intelligence, vol 1149. Springer, Cham. https://doi.org/10.1007/978-3-031-55684-5_26
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