Applying evolutionary algorithms to real-world-inspired problems with physical smoothness constraints

For the evaluation of evolutionary algorithms, the research community has devised numerous (artificial) test functions. In so doing, increasing emphasis has been devoted to “hard” problems, especially multimodal functions. This paper revisits some of the real-world applications originally used by Rechenberg (1997) and Schwefel (1997), and subsequently designs some similar artificial functions that can be conveniently used in computer experiments. Since “standard” evolutionary algorithms exhibit some performance difficulties on these test functions, this paper explores a specially-adapted algorithm, the optimize-and-refine procedure, which is an evolution strategy variant and inspired by common engineering methods. In the experiments, it turns out that on real-world-inspired test functions, the optimize-and-refine procedure yields a considerable speedup by exploiting certain constraints