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
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
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
"Optimal" mutation rates for genetic search
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Error thresholds in genetic algorithms
Evolutionary Computation
Parameter Setting in Evolutionary Algorithms
Parameter Setting in Evolutionary Algorithms
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
Zero is not a four letter word: studies in the evolution of language
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Amount and type of information: a GA-hardness taxonomy
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Optimizing monotone functions can be difficult
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
A RankMOEA to approximate the pareto front of a dynamic principal-agent model
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Investigation of genetic algorithms with self-adaptive crossover, mutation, and selection
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Investigation of self-adapting genetic algorithms using some multimodal benchmark functions
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
International Journal of Distributed Systems and Technologies
Entropy-based adaptive range parameter control for evolutionary algorithms
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Mutation rate matters even when optimizing monotonic functions
Evolutionary Computation
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Using a set of different search metrics and a set of model landscapes we theoretically and empirically study how "optimal" mutation rates for the simple genetic algorithm (SGA) depend not only on the fitness landscape, but also on population size and population state. We discuss the limitations of current mutation rate heuristics, showing that any fixed mutation rate can be expected to be suboptimal in terms of balancing exploration and exploitation. We then develop a mutation rate heuristic that offers a better balance by assigning different mutation rates to different subpopulations. When the mutation rate is assigned through a ranking of the population, according to fitness for example, we call the resulting algorithm a Rank GA. We show how this Rank GA overcomes the limitations of other heuristics on a set of model problems showing under what circumstances it might be expected to outperform a SGA with any choice of mutation rate.