Towards Low-Dimensional Gaussian Process Metamodels for CMA-ES

Gaussian processes and kriging models has attracted attention of researchers from different areas of black-box optimization, especially since Jones’ introduction of the Efficient Global Optimization (EGO) algorithm. However, current implementations of the EGO or real-world applications are rather few. We conjecture that the EGO is not suitable for higher-dimensional optimization and try to investigate whether hybridization of a low-dimensional local optimization with the current state-of-the-art continuous black-box optimizer CMA-ES (Covariance Matrix Adaptation Evolution Strategy) could help. In this paper, only a first proposal of such a GP/CMA-ES connection is described and some preliminary tests are presented