A meta-optimizer based on the upper confidence bound algorithm and orthogonal arrays: A preliminary empirical study

Article type: Research Article

Authors: Cosío-León, M.A. | Martínez-Vargas, Anabel^{; *} | Rodríguez-Cortés, Gabriela

Affiliations: Universidad Politécnica de Pachuca, Carretera Ciudad Sahagún-Pachuca Km. 20, Ex-Hacienda de Santa Bárbara, Hidalgo, México

Correspondence: [*] Corresponding author. Anabel Martínez-Vargas, Universidad Politécnica de Pachuca, Carretera Ciudad Sahagún-Pachuca Km. 20, Ex-Hacienda de Santa Bárbara, 43830, Hidalgo, México. Email: [email protected]

Abstract: It is well-known that tuning a metaheuristic is a critical task because the performance of a metaheuristic and the quality of its solutions depend on its parameter values. However, finding a good parameter setting is a time-consuming task. In this work, we apply the upper confidence bound (UCB) algorithm to automate offline tuning in a (1 + 1)-evolution strategy. Preliminary results show that our proposed approach is a less costly method.

Keywords: Upper confidence bound algorithm, meta-optimizer, bandit problems, reinforcement learning

DOI: 10.3233/JIFS-219362

Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024