Combining Gaussian processes, mutual information and a genetic algorithm for multi-target optimization of expensive-to-evaluate functions

Abstract

A novel approach to multi-target optimization of expensive-to-evaluate functions is explored that is based on a combined application of Gaussian processes, mutual information and a genetic algorithm. The aim of the approach is to find an approximation to the optimal solution (or the Pareto optimal solutions) within a small budget. The approach is shown to compare favourably with a surrogate based online evolutionary algorithm on two synthetic problems.