Gaussian Process Surrogate Models for the CMA Evolution Strategy

0498868 - ÚI 2020 RIV US eng J - Článek v odborném periodiku
Bajer, L. - Pitra, Z. - Repický, J. - Holeňa, Martin
Gaussian Process Surrogate Models for the CMA Evolution Strategy.
Evolutionary Computation. Roč. 27, č. 4 (2019), s. 665-697. ISSN 1063-6560. E-ISSN 1530-9304
Grant CEP: GA ČR GA17-01251S; GA ČR(CZ) GA18-18080S
Grant ostatní: GA MŠk(CZ) LM2015042
Institucionální podpora: RVO:67985807
Klíčová slova: Black-box optimization * CMA-ES * Gaussian processes * evolution strategies * surrogate modeling
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impakt faktor: 3.933, rok: 2019
Způsob publikování: Omezený přístup
http://dx.doi.org/10.1162/evco_a_00244

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.
Trvalý link: http://hdl.handle.net/11104/0291157