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
Improving Evolutionary Algorithms with Scouting: High---Dimensional Problems
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Constraint-handling techniques used with evolutionary algorithms
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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Experimental results reported in many papers suggest that making an appropriate choice of a method and its parameters to solve a nonlinear parameter optimisation problem remains an open question. The most promising approach at this stage of research seems to be experimental, involving a design of a scalable test suite of constrained optimisation problems. Then it would be possible to evaluate merits and drawbacks of the available optimisation methods as well as test new methods efficiently. In this paper we discuss the new test-case generator TCG-2 for constrained parameter optimisation techniques. The 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. This test-case generator is very useful for analysing and comparing different constraint-handling techniques and different nonlinear parameter optimisation techniques.