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Interaction between Model and its Evolution Control in Surrogate-assisted CMA Evolution Strategy
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SYSNO ASEP 0557942 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Interaction between Model and its Evolution Control in Surrogate-assisted CMA Evolution Strategy Author(s) Pitra, Z. (CZ)
Hanuš, M. (CZ)
Koza, J. (CZ)
Tumpach, Jiří (UIVT-O) ORCID, SAI
Holeňa, Martin (UIVT-O) SAI, RIDNumber of authors 5 Source Title Proceedings Of The 2021 Genetic And Evolutionary Computation Conference (Gecco'21). - New York : Association for Computing Machinery, 2021 / Chicano F. - ISBN 978-1-4503-8350-9 Pages s. 528-536 Number of pages 9 s. Publication form Print - P Action Gecco 2021: Genetic and Evolutionary Computation Conference Event date 10.07.2021 - 14.07.2021 VEvent location Lille / Online Country FR - France Event type WRD Language eng - English Country US - United States Keywords black-box optimization ; evolutionary optimization ; surrogate modelling ; evolution control ; CMA-ES OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) R&D Projects GA18-18080S GA ČR - Czech Science Foundation (CSF) Institutional support UIVT-O - RVO:67985807 UT WOS 000773791800063 EID SCOPUS 85110159996 DOI 10.1145/3449639.3459358 Annotation 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. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2023 Electronic address http://dx.doi.org/10.1145/3449639.3459358
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