An adaptive crossover distribution mechanism for genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
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
Investigation of genetic algorithms with self-adaptive crossover, mutation, and selection
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Self-adapting evolutionary parameters: encoding aspects for combinatorial optimization problems
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Hi-index | 0.00 |
Self-adaptive mutation, crossover, and selection were implemented and applied in three genetic algorithms. So developed self-adapting algorithms were then compared, with respect to convergence, with a standard genetic one, which contained constant rates of mutation and crossover. The experiments were conducted using five multimodal benchmark functions. The analysis of the results obtained was supported by nonparametric Friedman and Wilcoxon signed-rank tests. The algorithm employing self-adaptive selection revealed the best performance.