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Ordinal versus metric gaussian process regression in surrogate modelling for CMA evolution strategy
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SYSNO ASEP 0477787 Document Type A - Abstract R&D Document Type The record was not marked in the RIV R&D Document Type Není vybrán druh dokumentu Title Ordinal versus metric gaussian process regression in surrogate modelling for CMA evolution strategy Author(s) Pitra, Z. (CZ)
Bajer, L. (CZ)
Repický, J. (CZ)
Holeňa, Martin (UIVT-O) SAI, RIDSource Title GECCO 2017. Proceedings of the Genetic and Evolutionary Computation Conference Companion. - New York : ACM, 2017 - ISBN 978-1-4503-4939-0
S. 177-178Number of pages 2 s. Action GECCO 2017. Genetic and Evolutionary Computation Conference Event date 15.07.2017 - 19.07.2017 VEvent location Berlin Country DE - Germany Event type WRD Language eng - English Country US - United States Keywords black-box optimization ; evolutionary optimization ; surrogate modelling ; Gaussian-process regression Subject RIV IN - Informatics, Computer Science R&D Projects GA17-01251S GA ČR - Czech Science Foundation (CSF) Institutional support UIVT-O - RVO:67985807 DOI 10.1145/3067695.3076086 Annotation 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.
Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2018
Number of the records: 1