Adaptive crossover,mutation and selection using fuzzy system for genetic algorithms |
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Authors: | Soung-Min Im Ju-Jang Lee |
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Affiliation: | (1) Electrical Engineering and Computer Science, Korea Advanced Institute of Science and Technology (KAIST), 373-1 Guseong-dong, Yuseong-gu, Daejeon, 305-701, Korea |
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Abstract: | Genetic algorithms use a tournament selection or a roulette selection to choice better population. But these selections couldn’t
use heuristic information for specific problem. Fuzzy selection system by heuristic rule base help to find optimal solution
efficiently. And adaptive crossover and mutation probabilistic rate is faster than using fixed value. In this paper, we want
fuzzy selection system for genetic algorithms and adaptive crossover and mutation rate fuzzy system.
This work was presented in part and awarded as Young Author Award at the 13th International Symposium on Artificial Life and
Robotics, Oita, Japan, January 31–February 2, 2008 |
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Keywords: | Genetic algorithms (GA) Fuzzy logic system Adaptive genetic algorithm |
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