Interaction between Model and its Evolution Control in Surrogate-assisted CMA Evolution Strategy

0557942 - ÚI 2023 RIV US eng C - Konferenční příspěvek (zahraniční konf.)
Pitra, Z. - Hanuš, M. - Koza, J. - Tumpach, Jiří - Holeňa, Martin
Interaction between Model and its Evolution Control in Surrogate-assisted CMA Evolution Strategy.
Proceedings Of The 2021 Genetic And Evolutionary Computation Conference (Gecco'21). New York: Association for Computing Machinery, 2021 - (Chicano, F.), s. 528-536. ISBN 978-1-4503-8350-9.
[Gecco 2021: Genetic and Evolutionary Computation Conference. Lille / Online (FR), 10.07.2021-14.07.2021]
Grant CEP: GA ČR(CZ) GA18-18080S
Grant ostatní: Ministerstvo školství, mládeže a tělovýchovy - GA MŠk(CZ) LM2018140
Institucionální podpora: RVO:67985807
Klíčová slova: black-box optimization * evolutionary optimization * surrogate modelling * evolution control * CMA-ES
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://dx.doi.org/10.1145/3449639.3459358

Surrogate regression models have been shown as a valuable technique in evolutionary optimization to save evaluations of expensive black-box objective functions. Each surrogate modelling method has two complementary components: the employed model and the control of when to evaluate the model and when the true objective function, aka evolution control. They are often tightly interconnected, which causes difficulties in understanding the impact of each component on the algorithm performance. To contribute to such understanding, we analyse what constitutes the evolution control of three surrogate-assisted versions of the state-of-the-art algorithm for continuous black-box optimization --- the Covariance Matrix Adaptation Evolution Strategy. We implement and empirically compare all possible combinations of the regression models employed in those methods with the three evolution controls encountered in them. An experimental investigation of all those combinations allowed us to asses the influence of the models and their evolution control separately. The experiments are performed on the noiseless and noisy benchmarks of the Comparing-Continuous-Optimisers platform and a real-world simulation benchmark, all in the expensive scenario, where only a small budget of evaluations is available.
Trvalý link: http://hdl.handle.net/11104/0331826