A Surrogate Based Multiobjective Evolution Strategy with Different Models for Local Search and Pre-selection

In this paper we present a multiobjective evolutionary algorithm which uses surrogate models in two different ways -- during a local search and during pre-selection. Two different approaches to surrogate modeling are used, and the algorithm provides multiple individuals in each generation to enable easy parallelization. The algorithm is tested and compared to standard multiobjective evolutionary algorithms and to our previously developed surrogate evolution strategy. We also discuss the importance of the use of two different approaches and show that it improves the convergence speed significantly.