EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Test-Case Generator TCG-2 for Nonlinear Parameter Optimisation
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Adaptive Genetic Programming Applied to New and Existing Simple Regression Problems
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Experimental complexity analysis of continuous constraint satisfaction problems
Information Sciences: an International Journal
TCG-2: a test-case generator for non-linear parameter optimisation techniques
Advances in evolutionary computing
Methods for path and service planning under route constraints
International Journal of Computer Applications in Technology
A Generator for Multimodal Test Functions with Multiple Global Optima
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A new immune clone algorithm to solve the constrained optimization problems
WSEAS Transactions on Computers
The lay of the land: a brief survey of problem understanding
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Resampling versus repair in evolution strategies applied to a constrained linear problem
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 nonlinear parameter optimization problem remains an open question. It seems that the most promising approach at this stage of research is experimental, involving the design of a scalable test suite of constrained optimization 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 to test new methods efficiently. 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 different characteristics including: 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 multiple 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