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Ordinal versus metric gaussian process regression in surrogate modelling for CMA evolution strategy
- 1.0477787 - ÚI 2018 US eng A - Abstract
Pitra, Z. - Bajer, L. - Repický, J. - Holeňa, Martin
Ordinal versus metric gaussian process regression in surrogate modelling for CMA evolution strategy.
GECCO 2017. Proceedings of the Genetic and Evolutionary Computation Conference Companion. New York: ACM, 2017. s. 177-178. ISBN 978-1-4503-4939-0.
[GECCO 2017. Genetic and Evolutionary Computation Conference. 15.07.2017-19.07.2017, Berlin]
R&D Projects: GA ČR GA17-01251S
Grant - others:GA MŠk(CZ) LO1611; ČVUT(CZ) SGS17/193/OHK4/3T/14
Institutional support: RVO:67985807
Keywords : black-box optimization * evolutionary optimization * surrogate modelling * Gaussian-process regression
Subject RIV: IN - Informatics, Computer Science
This work presents an ordinal-based Gaussian process surrogate model for the state-of-the-art continuous black-box optimizer CMA-ES in scenarios where the objective evaluations are very expensive. Such model is motivated by the CMA-ES' invariance with respect to order preserving transformations. Alongside with the model's description, comparison with the standard (metric) Gaussian process surrogate for the CMA-ES is given.
Permanent Link: http://hdl.handle.net/11104/0274011
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