Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Self-adaptation in evolving systems
Artificial Life
Varying the Probability of Mutation in the Genetic Algorithm
Proceedings of the 3rd International Conference on Genetic Algorithms
Optimal Mutation Rates in Genetic Search
Proceedings of the 5th International Conference on Genetic Algorithms
Towards an Optimal Mutation Probability for Genetic Algorithms
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Setting The Mutation Rate: Scope And Limitations Of The 1/L Heuristic
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
"Optimal" mutation rates for genetic search
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Error thresholds in genetic algorithms
Evolutionary Computation
A similarity-based mating scheme for evolutionary multiobjective optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Evaluating las vegas algorithms: pitfalls and remedies
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Recombination of similar parents in EMO algorithms
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Theoretical analysis of rank-based mutation: combining exploration and exploitation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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We show how and why using genetic operators that are applied with probabilities that depend on the fitness rank of a genotype or phenotype offers a robust alternative to the Simple GA and avoids some questions of parameter tuning without having to introduce an explicit encoded self-adaptation mechanism. We motivate the algorithm by appealing to previous theoretic analysis that show how different landscapes and population states require different mutation rates to dynamically optimize the balance between exploration and exploitation. We test the algorithm on a range of model landscapes where we can see under what circumstances this Rank GA is likely to outperform the Simple GA and how it outperforms standard heuristics such as 1/N. We try to explain the reasons behind this behaviour.