Investigation of Gaussian Processes and Random Forests As Surrogate Models for Evolutionary Black-Box Optimization

Abstract

This paper introduces two surrogate models for continous black-box optimization, Gaussian processes and random forests, as an alternative to the already used ordinal SVM regression. We employ the CMA-ES as the reference optimization method with which the surrogate models are combined and also compared on subset of the noisless BBOB testing set.

Keywords

• Black-box optimization
• Surrogate model
• Gaussian process
• Random forest