• Title, Summary, Keyword: variable crossover and mutation probabilities

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A genetic algorithm with uniform crossover using variable crossover and mutation probabilities (동적인 교차 및 동연변이 확률을 갖는 균일 교차방식 유전 알고리즘)

  • Kim, Sung-Soo;Woo, Kwang-Bang
    • Journal of Institute of Control, Robotics and Systems
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    • v.3 no.1
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    • pp.52-60
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    • 1997
  • In genetic algorithms(GA), a crossover is performed only at one or two places of a chromosome, and the fixed probabilities of crossover and mutation have been used during the entire generation. A GA with dynamic mutation is known to be superior to GAs with static mutation in performance, but so far no efficient dynamic mutation method has been presented. Accordingly in this paper, a GA is proposed to perform a uniform crossover based on the nucleotide(NU) concept, where DNA and RNA consist of NUs and also a concrete way to vary the probabilities of crossover and mutation dynamically for every generation is proposed. The efficacy of the proposed GA is demonstrated by its application to the unimodal, multimodal and nonlinear control problems, respectively. Simulation results show that in the convergence speed to the optimal value, the proposed GA was superior to existing ones, and the performance of GAs with varying probabilities of the crossover and the mutation improved as compared to GAs with fixed probabilities of the crossover and mutation. And it also shows that the NUs function as the building blocks and so the improvement of the proposed algorithm is supported by the building block hypothesis.

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Numeric Pattern Recognition Using Genetic Algorithm and DNA coding (유전알고리즘과 DNA 코딩을 이용한 Numeric 패턴인식)

  • Paek, Dong-Hwa;Han, Seung-Soo
    • Journal of the Korean Institute of Intelligent Systems
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    • v.13 no.1
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    • pp.37-44
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    • 2003
  • In this paper, we investigated the performance of both DNA coding method and Genetic Algorithm(GA) in numeric pattern (from 0 to 9) recognition. The performance of the DNA coding method is compared to the that of the GA. GA searches effectively an optimal solution via the artificial evolution of individual group of binary string using binary coding, while DNA coding method uses four-type bases denoted by Adenine(A), Cytosine(C), Guanine(G) and Thymine(T). To compare the performance of both method, the same genetic operators(crossover and mutation) are applied and the probabilities of crossover and mutation are set the same values. The results show that the DNA coding method has better performance over GA. The reasons for this outstanding performance are multiple candidate solution presentation in one string and variable solution string length.