Improving Optimization With Gaussian Processes in the Covariance Matrix Adaptation Evolution Strategy

0579713 - ÚI 2024 RIV DE eng C - Konferenční příspěvek (zahraniční konf.)
Tumpach, J. - Koza, J. - Holeňa, Martin
Improving Optimization With Gaussian Processes in the Covariance Matrix Adaptation Evolution Strategy.
Proceedings of the 23st Conference Information Technologies – Applications and Theory (ITAT 2023). Aachen: Technical University & CreateSpace Independent Publishing, 2023 - (Brejová, B.; Ciencialová, L.; Holeňa, M.; Jajcay, R.; Jajcayová, T.; Lexa, M.; Mráz, F.; Pardubská, D.; Plátek, M.), s. 82-88. CEUR Workshop Proceedings, 3498. ISSN 1613-0073.
[ITAT 2023: Conference Information Technologies – Applications and Theory /23./. Tatranské Matliare (SK), 22.09.2023-26.09.2023]
Grant ostatní: Ministerstvo školství, mládeže a tělovýchovy - GA MŠk(CZ) LM2018140
Institucionální podpora: RVO:67985807
Klíčová slova: black-box optimization * covariance matrix adaptation evolution strategy * surrogate modelling * Gaussian processes
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://ceur-ws.org/Vol-3498/paper10.pdf

This paper explores the use of Gaussian processes (GPs) in the covariance matrix adaptation evolution strategy (CMA-ES) for black-box optimization. GPs are powerful probabilistic models that capture complex relationships, making them suitable for modeling uncertain objective functions. Integrating GPs into the CMA-ES improves exploration and adaptation in the search space, enhancing convergence speed and solution quality. The paper describes a novel implementation framework allowing to use GPs as surrogate models for the CMA-ES. That framework findings encourage further research to advance the application of GPs in black-box optimization.
Trvalý link: https://hdl.handle.net/11104/0348533