Gaussian Process Surrogate Models for the CMA-ES

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F19%3A00506867" target="_blank" >RIV/67985807:_____/19:00506867 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Gaussian Process Surrogate Models for the CMA-ES

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Gaussian Process Surrogate Models for the CMA-ES

Popis výsledku anglicky

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.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů