Test-case generator for nonlinear continuous parameter optimization techniques

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 e ciently. In this paper we propose such a test-case generator for constrained parameter optimization techniques. This generator is capable of creating various test problems with di erent characteristics, like (1) problems with di erent relative size of the feasible region in the search space; (2) problems with di erent number and types of constraints; (3) problems with convex or non-convex objective function, possibly with multiple optima; (4) problems with highly non-convex constraints consisting of (possibly) disjoint regions. Such a test-case generator is very useful for analyzing and comparing di erent constraint-handling techniques.