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Comparison of Ordinal and Metric Gaussian Process Regression as Surrogate Models for CMA Evolution Strategy
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SYSNO ASEP 0477789 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Comparison of Ordinal and Metric Gaussian Process Regression as Surrogate Models 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 Pages s. 1764-1771 Number of pages 8 s. Publication form Online - E 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 OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) R&D Projects GA17-01251S GA ČR - Czech Science Foundation (CSF) Institutional support UIVT-O - RVO:67985807 EID SCOPUS 85026863380 DOI 10.1145/3067695.3084206 Annotation In this paper, Gaussian processes are studied in connection with the state-of-the-art method for continuous black-box optimization CMA-ES. To combine them with the CMA-ES is challenging because CMA-ES invariance with respect to order preserving transformations suggests ordinal regression, whereas Gaussian process continuity suggests metric regression. Results of testing ordinal and metric Gaussian process regression, the former in 14 different settings, combined with the CMA-ES on noiseless benchmarks of the COCO platform are reported. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2018
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