Surrogate model selection for evolutionary multiobjective optimization

In surrogate evolutionary algorithms, usually the type of surrogate model is chosen beforehand, and it is never changed during the run of the evolution. Moreover, the reasoning why a particular type of model was chosen is often missing. In this paper, we present a framework which in each generation selects the most suitable surrogate from a set of models based on some pre-defined criteria. The results based on different types of model selectors are compared, and the dynamics of the evolution together with the change of the selected model type during the run of the evolutionary algorithm are discussed.