Abstrakt
This paper compares several Gaussian-process-based surrogate modeling methods applied to black-box optimization by means of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), which is considered state-of-the-art in the area of continuous black-box optimization. Among the compared methods are the Model-assisted CMA-ES, the Robust Kriging Metamodel CMA-ES, and the Surrogate CMA-ES.
In addition, a very successful surrogate-assisted self-adaptive CMA-ES, which is not based on Gaussian processes, but on ordinary regression by means of support vector machines has been included into the comparison. Those methods have been benchmarked using CEC'2013 testing functions.
We show that the surrogate CMA-ES achieves best results at the beginning and later phases of optimization process, conceding in the middle to surrogate-assisted CMA-ES.