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Overview of Surrogate-model Versions of Covariance Matrix Adaptation Evolution Strategy
- 1.0477762 - ÚI 2018 RIV US eng C - Conference Paper (international conference)
Pitra, Z. - Bajer, L. - Repický, J. - Holeňa, Martin
Overview of Surrogate-model Versions of Covariance Matrix Adaptation Evolution Strategy.
GECCO 2017. Proceedings of the Genetic and Evolutionary Computation Conference Companion. New York: ACM, 2017, s. 1622-1629. ISBN 978-1-4503-4939-0.
[GECCO 2017. Genetic and Evolutionary Computation Conference. Berlin (DE), 15.07.2017-19.07.2017]
R&D Projects: GA ČR GA17-01251S
Grant - others:GA MŠk(CZ) LO1611; ČVUT(CZ) SGS17/193/OHK4/3T/14; GA MŠk(CZ) LM2010005
Institutional support: RVO:67985807
Keywords : black-box optimization * evolutionary optimization * surrogate modelling
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Evaluation of real-world black-box objective functions is in many optimization problems very time-consuming or expensive. Therefore, surrogate regression models, used instead of the expensive objective function and in that way decreasing the number of its evaluations, have received a lot of attention. Here, we briefly survey surrogate-assisted versions of the state-of-the-art algorithm for continuous black-box optimization - the CMA-ES (Covariance Matrix Adaptation Evolution Strategy). We compare five of them, together with the original CMA-ES, on the noiseless benchmarks of the Comparing-Continuous-Optimisers platform in the expensive scenario, where only a small budget of evaluations is available.
Permanent Link: http://hdl.handle.net/11104/0274009
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