Evolution Strategies with Controlled Model Assistance
Accepted at the 2004 IEEE Congress on Evolutionary Computation in Portland, Oregon
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 pre-selecting 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 pre-selected
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.