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Gaussian Process Surrogate Models for the CMA Evolution Strategy
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SYSNO ASEP 0498868 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název Gaussian Process Surrogate Models for the CMA Evolution Strategy Tvůrce(i) Bajer, L. (CZ)
Pitra, Z. (CZ)
Repický, J. (CZ)
Holeňa, Martin (UIVT-O) SAI, RIDZdroj.dok. Evolutionary Computation. - : MIT Press - ISSN 1063-6560
Roč. 27, č. 4 (2019), s. 665-697Poč.str. 33 s. Jazyk dok. eng - angličtina Země vyd. US - Spojené státy americké Klíč. slova Black-box optimization ; CMA-ES ; Gaussian processes ; evolution strategies ; surrogate modeling Vědní obor RIV IN - Informatika Obor OECD Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) CEP GA17-01251S GA ČR - Grantová agentura ČR GA18-18080S GA ČR - Grantová agentura ČR Způsob publikování Omezený přístup Institucionální podpora UIVT-O - RVO:67985807 UT WOS 000500189000005 EID SCOPUS 85070618753 DOI 10.1162/evco_a_00244 Anotace 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. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2020 Elektronická adresa http://dx.doi.org/10.1162/evco_a_00244
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