Evolution strategies with controlled model assistance

Abstract

Evolutionary algorithms (EA) are excellent optimization tools for complex high-dimensional multimodal problems. However, they require a very large number of problem function evaluations. In many engineering and design optimization problems a single fitness evaluation is very expensive or time consuming. Therefore, standard evolutionary computation methods are not practical for such applications. Applying models as a surrogate of the true fitness function is a quite popular approach to handle this restriction. It is straightforward that the success of this approach depends highly on the quality of the approximation model. We propose a controlled model assisted evolution strategy (C-MAES), which uses a support vector regression (SVR) approximation by preselecting the most promising individuals. The model assistance on the evolutionary optimization process is dynamically controlled by a model quality based on the number of correctly preselected individuals. Numerical results from extensive simulations on high dimensional test functions including noisy functions and noisy functions with changing noise level are presented. The proposed C-MAES algorithm with controlled model assistance has a much better convergence rate and achieves better results than the model assisted algorithms without model control.