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Gaussian Process Surrogate Models for the CMA-ES
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SYSNO ASEP 0506867 Document Type A - Abstract R&D Document Type O - Ostatní Title Gaussian Process Surrogate Models for the CMA-ES Author(s) Bajer, Lukáš (UIVT-O) SAI, RID, ORCID
Pitra, Zbyněk (UIVT-O) RID, ORCID, SAI
Repický, Jakub (UIVT-O) ORCID, SAI
Holeňa, Martin (UIVT-O) SAI, RIDSource Title GECCO '19. Proceedings of the Genetic and Evolutionary Computation Conference Companion. - New York : ACM, 2019 - ISBN 978-1-4503-6748-6
S. 17-18Number of pages 2 s. Publication form Print - P Action GECCO 2019: The Genetic and Evolutionary Computation Conference Event date 13.07.2019 - 17.07.2019 VEvent location Prague Country CZ - Czech Republic Event type WRD Language eng - English Country US - United States Keywords black-box optimization ; evolutionary optimization ; surrogate modelling ; Gaussian process 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) Institutional support UIVT-O - RVO:67985807 UT WOS 000538328100009 EID SCOPUS 85070636233 DOI 10.1145/3319619.3326764 Annotation IN: GECCO '19. Proceedings of the Genetic and Evolutionary Computation Conference Companion. New York: ACM, 2019. s. 17-18. ISBN 978-1-4503-6748-6. CONFERENCE: GECCO 2019: The Genetic and Evolutionary Computation Conference. 13.07.2019-17.07.2019, Prague. PROJECT: GA ČR GA17-01251S, GA ČR(CZ) GA18-18080S. This extended abstract previews the usage of Gaussian processes in a surrogate-model version of the CMA-ES, a state-of-the-art black-box continuous optimization algorithm. The proposed algorithm DTS-CMA-ES exploits the benefits of Gaussian process uncertainty prediction, especially during the selection of points for the evaluation with the surrogate model. Very brief results are presented here, while much more elaborate description of the methods, parameter settings and detailed experimental results can be found in the original article Gaussian Process Surrogate Models for the CMA Evolution Strategy, to appear in the Evolutionary Computation. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2020
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