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Investigation of Gaussian Processes and Random Forests as Surrogate Models for Evolutionary Black-Box Optimization
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SYSNO ASEP 0446913 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Investigation of Gaussian Processes and Random Forests as Surrogate Models for Evolutionary Black-Box Optimization Author(s) Bajer, Lukáš (UIVT-O) SAI, RID, ORCID
Pitra, Z. (CZ)
Holeňa, Martin (UIVT-O) SAI, RIDSource Title GECCO Companion '15. Genetic and Evolutionary Computation Conference. Companion Material Proceedings. - New York : ACM, 2015 / Silva S. - ISBN 978-1-4503-3488-4 Pages s. 1351-1352 Number of pages 2 s. Publication form Online - E Action GECCO Companion '15. Genetic and Evolutionary Computation Conference Event date 11.07.2015-15.07.2015 VEvent location Madrid Country ES - Spain Event type WRD Language eng - English Country US - United States Keywords Black-box optimization ; Surrogate model ; Gaussian process ; Random forest Subject RIV IN - Informatics, Computer Science R&D Projects GA13-17187S GA ČR - Czech Science Foundation (CSF) Institutional support UIVT-O - RVO:67985807 EID SCOPUS 84959386240 DOI 10.1145/2739482.2764692 Annotation This paper introduces two surrogate models for continous black-box optimization, Gaussian processes and random forests, as an alternative to the already used ordinal SVM regression. We employ the CMA-ES as the reference optimization method with which the surrogate models are combined and also compared on subset of the noisless BBOB testing set. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2016
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