Test-case generator for nonlinear continuous parameter optimization techniques

The experimental results reported in many papers suggest that making an app ropriate a priori choice of an evolutionary method for a nonlinear paramete r optimization problem remains an open question. It seems that the most pro mising approach at this stage of research is experimental, involving the de sign of a scalable test suite of constrained optimization problems, in whic h many features could be tuned easily. It would then be possible to evaluat e the merits and drawbacks of the available methods, as well as to test new methods efficiently. In this paper, we propose such a test-case generator for constrained parameter optimization techniques. This generator is capabl e of creating various test problems with different characteristics includin g: 1) problems with different relative sizes of the feasible region in the search space; 2) problems with different numbers and types of constraints; 3) problems with convex or nonconvex evaluation functions, possibly with mu ltiple optima; and 4) problems with highly nonconvex constraints consisting of (possibly) disjoint regions. Such a test-case generator is very useful for analyzing and comparing different constraint-handling techniques.