Abstract
Objective function evaluation in continuous optimization tasks is often the operation that dominates the algorithm's cost. In particular in the case of black-box functions, i.e. when no analytical description is available, and the function is evaluated empirically.
In such a situation, utilizing information from a surrogate model of the objective function is a well known technique to accelerate the search. In this paper, we review two traditional approaches to surrogate modelling based on Gaussian processes that we have newly reimplemented in MATLAB: Metamodel Assisted Evolution Strategy using probability of improvement and Gaussian Process Optimization Procedure.
In the research reported in this paper, both approaches have been for the first time evaluated on Black-Box Optimization Benchmarking framework (BBOB), a comprehensive benchmark for continuous optimizers.