Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Intelligent Mutation Rate Control in Canonical Genetic Algorithms
ISMIS '96 Proceedings of the 9th International Symposium on Foundations of Intelligent Systems
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
On the log-normal self-adaptation of the mutation rate in binary search spaces
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Application-aware deadlock-free oblivious routing based on extended turn-model
Proceedings of the International Conference on Computer-Aided Design
Static and adaptive mutation techniques for genetic algorithm: a systematic comparative analysis
International Journal of Computational Science and Engineering
Impact of static and adaptive mutation techniques on the performance of Genetic Algorithm
International Journal of Hybrid Intelligent Systems
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Self-adaptation is used a lot in Evolutionary Strategies and with great success, yet for some reason it is not the mutation adaptation of choice for Genetic Algorithms. This poster describes how a self-adaptive mutation rate was used in a Genetic Algorithms to inverse design behavioral rules for a Cellular Automata. The unique characteristics of this search space gave rise to some interesting convergence behavior that might have implications for using self-adaptive mutation rates in other Genetic Algorithm applications and might clarify why self-adaptation in Genetic Algorithms is less successful than in Evolutionary Strategies.