The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Varying the Probability of Mutation in the Genetic Algorithm
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 6th 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
Adapting operator settings in genetic algorithms
Evolutionary Computation
Diversity improvement by non-geometric binary crossover in evolutionary multiobjective optimization
IEEE Transactions on Evolutionary Computation
A parameter-less genetic algorithm with customized crossover and mutation operators
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Intelligent Social Media Indexing and Sharing Using an Adaptive Indexing Search Engine
ACM Transactions on Intelligent Systems and Technology (TIST)
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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In this paper, a new gene based adaptive mutation scheme is proposed for genetic algorithms (GAs), where the information on gene based fitness statistics and on gene based allele distribution statistics are correlated to explicitly adapt the mutation probability for each gene locus over time. A convergence control mechanism is combined with the proposed mutation scheme to maintain sufficient diversity in the population. Experiments are carried out to compare the proposed mutation scheme to traditional mutation and two advanced adaptive mutation schemes on a set of optimization problems. The experimental results show that the proposed mutation scheme efficiently improves GA's performance.