Meta-learning and Model Selection in Multi-objective Evolutionary Algorithms

Most existing surrogate based evolutionary algorithms deal with only one model selected by the authors and different models are not considered. In this paper we propose a framework which enables automatic selection of types of surrogate models, and evaluate the effect of the type of selection on the overall performance of the resulting evolutionary algorithm. Two different types of model selection are tested and compared both in pre-selection scenario and in local search scenario.