0506867 - ÚI 2020 RIV US eng A - Abstrakt
Bajer, Lukáš - Pitra, Zbyněk - Repický, Jakub - Holeňa, Martin
Gaussian Process Surrogate Models for the CMA-ES.
GECCO '19. Proceedings of the Genetic and Evolutionary Computation Conference Companion. New York: ACM, 2019. s. 17-18. ISBN 978-1-4503-6748-6.
[GECCO 2019: The Genetic and Evolutionary Computation Conference. 13.07.2019-17.07.2019, Prague]
Grant CEP: GA ČR GA17-01251S; GA ČR(CZ) GA18-18080S
Institucionální podpora: RVO:67985807
Klíčová slova: black-box optimization * evolutionary optimization * surrogate modelling * Gaussian process
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
DOI: https://doi.org/10.1145/3319619.3326764

IN: GECCO '19. Proceedings of the Genetic and Evolutionary Computation Conference Companion. New York: ACM, 2019. s. 17-18. ISBN 978-1-4503-6748-6. CONFERENCE: GECCO 2019: The Genetic and Evolutionary Computation Conference. 13.07.2019-17.07.2019, Prague. PROJECT: GA ČR GA17-01251S, GA ČR(CZ) GA18-18080S. This extended abstract previews the usage of Gaussian processes in a surrogate-model version of the CMA-ES, a state-of-the-art black-box continuous optimization algorithm. The proposed algorithm DTS-CMA-ES exploits the benefits of Gaussian process uncertainty prediction, especially during the selection of points for the evaluation with the surrogate model. Very brief results are presented here, while much more elaborate description of the methods, parameter settings and detailed experimental results can be found in the original article Gaussian Process Surrogate Models for the CMA Evolution Strategy, to appear in the Evolutionary Computation.
Trvalý link: http://hdl.handle.net/11104/0298001