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Comparing SVM, Gaussian Process and Random Forest Surrogate Models for the CMA-ES.

, , and . ITAT, volume 1422 of CEUR Workshop Proceedings, page 186-193. CEUR-WS.org, (2015)

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Gaussian process surrogate models for the CMA-ES., , , and . GECCO (Companion), page 17-18. ACM, (2019)Investigation of Gaussian Processes and Random Forests as Surrogate Models for Evolutionary Black-Box Optimization., , and . GECCO (Companion), page 1351-1352. ACM, (2015)Comparison of ordinal and metric gaussian process regression as surrogate models for CMA evolution strategy., , , and . GECCO (Companion), page 1764-1771. ACM, (2017)Doubly Trained Evolution Control for the Surrogate CMA-ES., , and . PPSN, volume 9921 of Lecture Notes in Computer Science, page 59-68. Springer, (2016)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)Investigation of Gaussian Processes in the Context of Black-Box Evolutionary Optimization., , , and . ITAT, volume 1422 of CEUR Workshop Proceedings, page 159-166. CEUR-WS.org, (2015)Ordinal versus metric gaussian process regression in surrogate modelling for CMA evolution strategy., , , and . GECCO (Companion), page 177-178. ACM, (2017)Benchmarking Gaussian Processes and Random Forests Surrogate Models on the BBOB Noiseless Testbed., , and . GECCO (Companion), page 1143-1150. ACM, (2015)