A self-organizing genetic algorithm for multimodal function optimization
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A genetic algorithm (GA) has control parameters that must be determined before execution. We propose a self-organizing genetic algorithm (SOGA) as a multimodal function optimizer which sets GA parameters such as population size, crossover probability, and mutation probability adaptively during the execution of a genetic algorithm. In SOGA, GA parameters change according to the fitnesses of individuals. SOGA and other approaches for adapting operator probabilities in GAs are discussed. The validity of the proposed algorithm is verified in simulation examples, including system identification.
Key wordsGenetic algorithm Self-organizing
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- 2.Jeong IK, Lee JJ (1994) Genetic algorithms and neural networks for identification and control. Proceedings of the first Asian control conference, Japan, pp 697–700Google Scholar
- 3.Jeong IK, Lee JJ (1995) A modified genetic algorithm for neurocontrollers. IEEE international conference on evolutionary computation, Australia, pp 306–311Google Scholar
- 7.Julstrom BA (1995) What have you done for me lately? Adapting operator probabilities in a steady-state genetic algorithm. Proceedings of the 6th international conference on genetic algorithms, pp 81–87Google Scholar
- 8.Smith J, Fogarty TC (1996) Self adaptation of mutation rates in a steady state genetic algorithm. IEEE International Conference on Evolutionary Computation, Japan, pp 318–323Google Scholar