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Overview of surrogate-model versions of covariance matrix adaptation evolution strategy.

, , , and . GECCO (Companion), page 1622-1629. ACM, (2017)

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Gaussian process surrogate models for the CMA-ES., , , and . GECCO (Companion), page 17-18. ACM, (2019)Overview of surrogate-model versions of covariance matrix adaptation evolution strategy., , , and . GECCO (Companion), page 1622-1629. ACM, (2017)Landscape analysis of gaussian process surrogates for the covariance matrix adaptation evolution strategy., , and . GECCO, page 691-699. ACM, (2019)Traditional Gaussian Process Surrogates in the BBOB Framework., , and . ITAT, volume 1649 of CEUR Workshop Proceedings, page 163-171. CEUR-WS.org, (2016)Comparison of ordinal and metric gaussian process regression as surrogate models for CMA evolution strategy., , , and . GECCO (Companion), page 1764-1771. ACM, (2017)Adaptive Generation-Based Evolution Control for Gaussian Process Surrogate Models., , , and . CoRR, (2017)Transfer of Knowledge for Surrogate Model Selection in Cost-Aware Optimization., , and . IAL@PKDD/ECML, volume 2192 of CEUR Workshop Proceedings, page 89-94. CEUR-WS.org, (2018)Adaptive Selection of Gaussian Process Model for Active Learning in Expensive Optimization., , and . IAL@PKDD/ECML, volume 2192 of CEUR Workshop Proceedings, page 80-84. CEUR-WS.org, (2018)Ordinal versus metric gaussian process regression in surrogate modelling for CMA evolution strategy., , , and . GECCO (Companion), page 177-178. ACM, (2017)