Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Strategy Parameter Variety In Self-adaptation Of Mutation Rates
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
An Experimental Investigation of Self-Adaptation in Evolutionary Programming
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
An analysis of mutative σ-self-adaptation on linear fitness functions
Evolutionary Computation
On three new approaches to handle constraints within evolution strategies
Natural Computing: an international journal
A review of constraint-handling techniques for evolution strategies
Applied Computational Intelligence and Soft Computing - Special issue on theory and applications of evolutionary computation
Variance scaling for EDAs revisited
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Information Sciences: an International Journal
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The optimum of numerical problems quite often lies on the constraint boundary or even in a vertex of the feasible search space. In such cases the evolutionary algorithm (EA) frequently suffers from premature convergence because of a low success probability near the constraint boundaries. We analyze premature fitness stagnation and the success rates experimentally for an EA using self-adaptive step size control. For a (1+1)-EA with a Rechenberg-like step control mechanism we prove premature step size reduction at the constraint boundary. The proof is based on a success rate analysis considering a simplified mutation distribution model. From the success rates and the possible state transitions, the expected step size change can be derived at each step. We validate the theoretical model with an experimental analysis.