The theory of evolution strategies
The theory of evolution strategies
Where Elitists Start Limping Evolution Strategies at Ridge Functions
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Step-Size Adaption Based on Non-Local Use of Selection Information
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Weighted multirecombination evolution strategies
Theoretical Computer Science - Foundations of genetic algorithms
Algorithmic analysis of a basic evolutionary algorithm for continuous optimization
Theoretical Computer Science
Step length adaptation on ridge functions
Evolutionary Computation
Mirrored sampling in evolution strategies with weighted recombination
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A median success rule for non-elitist evolution strategies: study of feasibility
Proceedings of the 15th annual conference on Genetic and evolutionary computation
A median success rule for non-elitist evolution strategies: study of feasibility
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Success rule based step-size adaptation, namely the one-fifth success rule, has shown to be effective for single parent evolution strategies (ES), e.g. the (1+1)-ES. The success rule remains feasible in non-elitist single parent strategies, where the target success rate must be roughly inversely proportional to the population size. This success rule is, however, not easily applicable to multi-parent strategies. In this paper, we introduce the median success rule for step-size adaptation, applicable to non-elitist multi-recombinant evolution strategies. The median success rule compares the median fitness of the population to a fitness from the previous iteration. The comparison fitness is chosen to achieve a target success rate of 1/2, thereby a deviation from the target can be measured reliably in comparatively few iteration steps. As a prerequisite for feasibility of the median success rule, we studied the way the fitness comparison quantile depends on the search space dimension, the population size, the parent number, the recombination weights and the objective function. The findings are encouraging: the choice of the comparison quantile appears to be relatively uncritical and experiments on a variety of functions, also in combination with CMA, reveal reasonable behavior.