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Benchmarking Gaussian Processes and Random Forests Surrogate Models on the BBOB Noiseless Testbed
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SYSNO ASEP 0446912 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Benchmarking Gaussian Processes and Random Forests Surrogate Models on the BBOB Noiseless Testbed 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. 1143-1150 Number of pages 8 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 benchmarking ; 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 84959386448 DOI 10.1145/2739482.2768468 Annotation Speeding-up black-box optimization algorithms via learning and using a surrogate model is a heavily studied topic. This paper evaluates two different surrogate models: Gaussian processes and random forests which are interconnected with the state-of-the art optimization algorithm CMA-ES. Results on the BBOB testing set show that considerable amount of fitness evaluations can be saved especially during the initial phase of the algorithm's progress. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2016
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