How to solve it: modern heuristics
How to solve it: modern heuristics
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
A derandomized approach to self-adaptation of evolution strategies
Evolutionary Computation
Self-Adaptive Heuristics for Evolutionary Computation
Self-Adaptive Heuristics for Evolutionary Computation
A review of constraint-handling techniques for evolution strategies
Applied Computational Intelligence and Soft Computing - Special issue on theory and applications of evolutionary computation
A (1+1)-CMA-ES for constrained optimisation
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Evolutionary algorithm characterization in real parameter optimization problems
Applied Soft Computing
Towards non-linear constraint estimation for expensive optimization
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
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Many practical optimization problems are constrained black boxes. Covariance Matrix Adaptation Evolution Strategies (CMA-ES) belong to the most successful black box optimization methods. Up to now no sophisticated constraint handling method for Covariance Matrix Adaptation optimizers has been proposed. In our novel approach we learn a meta-model of the constraint function and use this surrogate model to adapt the covariance matrix during the search at the vicinity of the constraint boundary. The meta-model can be used for various purposes, i.e. rotation of the mutation ellipsoid, checking the feasibility of candidate solutions or repairing infeasible mutations by projecting them onto the constraint surrogate function. Experimental results show the potentials of the proposed approach.