Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic algorithm parameter sets for line labelling
Pattern Recognition Letters - special issue on pattern recognition in practice V
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Proceedings of the 3rd International Conference on Genetic Algorithms
Intelligent Mutation Rate Control in Canonical Genetic Algorithms
ISMIS '96 Proceedings of the 9th International Symposium on Foundations of Intelligent Systems
Practical Genetic Algorithms with CD-ROM
Practical Genetic Algorithms with CD-ROM
Adaptive mutation with fitness and allele distribution correlation for genetic algorithms
Proceedings of the 2006 ACM symposium on Applied computing
An improved genetic algorithm with initial population strategy and self-adaptive member grouping
Computers and Structures
Self-adaptive mutation rates in genetic algorithm for inverse design of cellular automata
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Assortative mating in genetic algorithms for dynamic problems
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
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In this paper, a systematic comparative analysis is presented on various static and adaptive mutation techniques to understand their nature on genetic algorithm. Three most popular random mutation techniques such as uniform mutation, Gaussian mutation and boundary mutation, two recently introduced individual adaptive mutation techniques, a self-adaptive mutation technique and a deterministic mutation technique are taken to carry out the analysis. A common experimental bench of benchmark test functions is used to test the techniques and the results are analysed. The analysis intends to identify a best mutation technique for every benchmark problem and to understand the dependency behaviour of mutation techniques with other genetic algorithm parameters such as population sizes, crossover rates and number of generations. Based on the analytical results, interesting findings are obtained that would improve the performance of genetic algorithm.