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Gaussian Process Surrogate Models for the CMA Evolution Strategy
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SYSNO ASEP 0498868 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Gaussian Process Surrogate Models for the CMA Evolution Strategy Author(s) Bajer, L. (CZ)
Pitra, Z. (CZ)
Repický, J. (CZ)
Holeňa, Martin (UIVT-O) SAI, RIDSource Title Evolutionary Computation. - : MIT Press - ISSN 1063-6560
Roč. 27, č. 4 (2019), s. 665-697Number of pages 33 s. Language eng - English Country US - United States Keywords Black-box optimization ; CMA-ES ; Gaussian processes ; evolution strategies ; surrogate modeling Subject RIV IN - Informatics, Computer Science OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) R&D Projects GA17-01251S GA ČR - Czech Science Foundation (CSF) GA18-18080S GA ČR - Czech Science Foundation (CSF) Method of publishing Limited access Institutional support UIVT-O - RVO:67985807 UT WOS 000500189000005 EID SCOPUS 85070618753 DOI 10.1162/evco_a_00244 Annotation This article deals with Gaussian process surrogate models for the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES)—several already existing and two by the authors recently proposed models are presented. The work discusses different variants of surrogate model exploitation and focuses on the benefits of employing the Gaussian process uncertainty prediction, especially during the selection of points for the evaluation with a surrogate model. The experimental part of the paper thoroughly compares and evaluates the five presented Gaussian process surrogate and six other state-of-the-art optimizers on the COCO benchmarks. The algorithm presented in most detail, DTS-CMA-ES, which combines cheap surrogate-model predictions with the objective function evaluations in every iteration, is shown to approach the function optimum at least comparably fast and often faster than the state-of-the-art black-box optimizers for budgets of roughly 25–100 function evaluations per dimension, in 10- and lessdimensional spaces even for 25–250 evaluations per dimension. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2020 Electronic address http://dx.doi.org/10.1162/evco_a_00244
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