Gaussian process surrogate models for the CMA-ES
Lukáš Bajer, Zbyněk Pitra, Jakub Repický, Martin Holeňa · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2019
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 [2], to appear in the Evolutionary Computation1.