Evaluating evolutionary algorithms
Artificial Intelligence - Special volume on empirical methods
Building Better Test Functions
Proceedings of the 6th International Conference on Genetic Algorithms
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
Test-case generator for nonlinear continuous parameter optimizationtechniques
IEEE Transactions on Evolutionary Computation
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The experimental results reported in many papers suggest that making an appropriate a priori choice of an evolutionary method for a non-linear parameter optimisation 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 optimisation problems, in which many features could be tuned easily. It would then be possible to evaluate the merits and drawbacks of the available methods as well as test new methods efficiently. In this chapter, we discuss a new test-case generator for constrained parameter optimisation techniques, which deals with deficiencies of the generators proposed earlier. This generator, TCG-2, is capable of creating various test problems with different characteristics, including the dimensionality of the problem, number of local optima, number of active constraints at the optimum, topology of the feasible search space, etc. Such a test-case generator is very useful for analysing and comparing different constraint-handling techniques.