Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Self-adaptation in evolving systems
Artificial Life
Effective Fitness as an Alternative Paradigm for Evolutionary Computation I: General Formalism
Genetic Programming and Evolvable Machines
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
Setting The Mutation Rate: Scope And Limitations Of The 1/L Heuristic
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Error thresholds in genetic algorithms
Evolutionary Computation
Adopting dynamic operators in a genetic algorithm
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Rank based variation operators for genetic algorithms
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Limitations of existing mutation rate heuristics and how a rank GA overcomes them
IEEE Transactions on Evolutionary Computation
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Using a set of model landscapes we examine how different mutation rates affect different search metrics. We show that very universal heuristics, such as 1/N and the error threshold, can generally be improved upon if one has some qualitative information about the landscape. In particular, we show in the case of multiple optima (signals) how mutation affects which signal dominates and how passing between the dominance of one to another depends on the relative height and size of the peaks and their relative positions in the configuration space.