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Investigation of Gaussian Processes and Random Forests as Surrogate Models for Evolutionary Black-Box Optimization
- 1.0446913 - ÚI 2016 RIV US eng C - Conference Paper (international conference)
Bajer, Lukáš - Pitra, Z. - Holeňa, Martin
Investigation of Gaussian Processes and Random Forests as Surrogate Models for Evolutionary Black-Box Optimization.
GECCO Companion '15. Genetic and Evolutionary Computation Conference. Companion Material Proceedings. New York: ACM, 2015 - (Silva, S.), s. 1351-1352. ISBN 978-1-4503-3488-4.
[GECCO Companion '15. Genetic and Evolutionary Computation Conference. Madrid (ES), 11.07.2015-15.07.2015]
R&D Projects: GA ČR GA13-17187S
Grant - others:ČVUT(CZ) SGS14/205/OHK4/3T/14; GA MŠk(CZ) ED2.1.00/03.0078
Institutional support: RVO:67985807
Keywords : Black-box optimization * Surrogate model * Gaussian process * Random forest
Subject RIV: IN - Informatics, Computer Science
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.
Permanent Link: http://hdl.handle.net/11104/0248875
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