Comparison of ordinal and metric gaussian process regression as surrogate models for CMA evolution strategy
Zbyněk Pitra, Lukáš Bajer, Jakub Repický, Martin Holeňa · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2017
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.