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Adaptive crossover, mutation and selection using fuzzy system for genetic algorithms

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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.

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Authors and Affiliations

  1. Electrical Engineering and Computer Science, Korea Advanced Institute of Science and Technology (KAIST), 373-1 Guseong-dong, Yuseong-gu, Daejeon, 305-701, Korea

    Soung-Min Im & Ju-Jang Lee

Authors
  1. Soung-Min Im
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  2. Ju-Jang Lee
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Corresponding author

Correspondence to Soung-Min Im.

Additional information

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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Cite this article

Im, SM., Lee, JJ. Adaptive crossover, mutation and selection using fuzzy system for genetic algorithms. Artif Life Robotics 13, 129–133 (2008). https://doi.org/10.1007/s10015-008-0545-1

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  • DOI: https://doi.org/10.1007/s10015-008-0545-1

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