{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T19:02:29Z","timestamp":1787166149943,"version":"build-2736575974"},"reference-count":43,"publisher":"MIT Press","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Evolutionary Computation"],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:p>This article deals with Gaussian process surrogate models for the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES)\u2014several 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 article 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\u2013100 function evaluations per dimension, in 10- and less-dimensional spaces even for 25\u2013250 evaluations per dimension.<\/jats:p>","DOI":"10.1162\/evco_a_00244","type":"journal-article","created":{"date-parts":[[2018,12,12]],"date-time":"2018-12-12T13:51:31Z","timestamp":1544622691000},"page":"665-697","source":"Crossref","is-referenced-by-count":48,"title":["Gaussian Process Surrogate Models for the CMA Evolution Strategy"],"prefix":"10.1162","volume":"27","author":[{"given":"Luk\u00e1\u0161","family":"Bajer","sequence":"first","affiliation":[{"name":"Faculty of Mathematics and Physics, Charles University in Prague, Malostran. n\u00e1m. 25, 118 00 Prague, Czech Republic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zbyn\u011bk","family":"Pitra","sequence":"additional","affiliation":[{"name":"Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University B\u0159ehov\u00e1 7, 115 19 Prague, Czech Republic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jakub","family":"Repick\u00fd","sequence":"additional","affiliation":[{"name":"Faculty of Mathematics and Physics, Charles University in Prague, Malostran. n\u00e1m. 25, 118 00 Prague, Czech Republic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Hole\u0148a","sequence":"additional","affiliation":[{"name":"Institute of Computer Science, Czech Academy of Sciences, Pod Vod\u00e1renskou v\u011b\u017e\u00ed 2, 182 07 Prague, Czech Republic"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"281","reference":[{"key":"B1","first-page":"1225","author":"Auger A.","year":"2013","journal-title":"Proceedings of the 15th Annual Conference Companion"},{"key":"B2","first-page":"182","author":"Auger A.","year":"2004","journal-title":"Parallel Problem Solving from Nature"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1142\/p620"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1145\/2739482.2768468"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1002\/jnm.2088"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1145\/3071178.3071343"},{"key":"B7","volume-title":"Algorithms for minimization without derivatives","author":"Brent R. 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