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
On initial populations of a genetic algorithm for continuous optimization problems
Journal of Global Optimization
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
Assortative mating in genetic algorithms for dynamic problems
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Genetic algorithm based airlines booking terminal open/close decision system
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
International Journal of Hybrid Intelligent Systems
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Genetic Algorithm GA is one of the most popular heuristic search algorithms inspired by nature's evolutionary behavior. Among the various genetic operators, mutation is one important operator that helps to accelerate the searching ability of GA. As GA finds numerous applications, it undergoes various enhancements and modifications, especially with respect to mutation operator. Numerous mutation techniques have been reported in the literature that can be broadly categorized into static and adaptive mutation techniques. This work selectively analyzes six mutation techniques in a common bench of experiments. Among the six mutation techniques, two are the popular variants of static mutation techniques called as Uniform mutation and Gaussian Mutation. The remaining four were recently introduced: two individual adaptive mutation techniques, a self adaptive mutation technique and a deterministic mutation technique. Totally, 28 benchmark functions, which fall under the benchmark categories of unimodal, multimodal, extended multimodal, diagonal and quadratic functions, are used in the work. The analysis mainly intends to determine a best mutation technique for every benchmark problem and to understand the dependency behavior of mutation techniques with other GA parameters such as crossover probabilities, population sizes and number of generations. It leads to interesting findings which would help to improve the GA performance on other practical and benchmark problems.