Self-Adaptive Genetic Algorithms with Simulated Binary Crossover
Evolutionary Computation
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
Investigation of evolutionary optimization methods of TSK fuzzy model for real estate appraisal
International Journal of Hybrid Intelligent Systems - Recent Advances in Intelligent Paradigms Fusion and Their Applications
Limitations of existing mutation rate heuristics and how a rank GA overcomes them
IEEE Transactions on Evolutionary Computation
Self-adapting evolutionary parameters: encoding aspects for combinatorial optimization problems
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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
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A method of self-adaptive mutation, crossover and selection was implemented and applied in four genetic algorithms. So developed self-adapting algorithms were then compared, with respect to convergence, with a traditional genetic one, which contained constant rates of mutation and crossover. The experiments were conducted on six benchmark functions including two unimodal functions, three multimodal with many local minima, and one multimodal with a few local minima. The analysis of the results obtained was supported by statistical nonparametric Wilcoxon signed-rank tests. The algorithm employing self-adaptive selection revealed the best performance.