Towards understanding constraint-handling methods in evolutionary algorithms

The experimental results reported in many papers suggest that making an appropriate a priori choice of an evolutionary method for a nonlinear parameter optimization problem remains an open question. It seems that the most promising approach at this stage of research is experimental, involving a design of a scalable test suite of constrained optimization problems, in which many features could be easily tuned. Then it would be possible to evaluate merits and drawbacks of the available methods as well as test new methods efficiently. In this paper we discuss a recently proposed test-case generator for constrained parameter optimization techniques. This generator is capable of creating various test cases with different characteristics and is very useful for analyzing and comparing different constraint-handling techniques