Gaussian Process Surrogate Models for the CMA Evolution Strategy

0498868 - ÚI 2020 RIV US eng J - Journal Article
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
R&D Projects: GA ČR GA17-01251S; GA ČR(CZ) GA18-18080S
Grant - others:GA MŠk(CZ) LM2015042
Institutional support: RVO:67985807
Keywords : Black-box optimization * CMA-ES * Gaussian processes * evolution strategies * surrogate modeling
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 3.933, year: 2019
Method of publishing: Limited access
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.
Permanent Link: http://hdl.handle.net/11104/0291157