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Article Navigation >  Journal of University of Electronic Science and Technology of China  > 2002  >  31(3): 234-239

Zhang Yu, Guo Jing, Zhou Jiliu. Dynamic Mutation Genetic Algorithm[J]. Journal of University of Electronic Science and Technology of China, 2002, 31(3): 234-239.
Citation: Zhang Yu, Guo Jing, Zhou Jiliu. Dynamic Mutation Genetic Algorithm[J]. Journal of University of Electronic Science and Technology of China, 2002, 31(3): 234-239.
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Dynamic Mutation Genetic Algorithm

  • 1.

    Dept,of Electronic Engineering,Chengdu Aeronautic Vocational & Technical College Chengdu 610061;

  • 2.

    College of Electronic and Information,Sichuan University Chengdu 610064

  • Received Date: 2002-01-19
  • Publish Date: 2002-06-15
  • Genetic Algorithms is Optimal Algorithm, which employ a search technique based on ideas from Darwin's natural evolution theory. A new genetic algorithm (NGA) is proposed in this paper, which not only can keep the population diversity but also has quicker convergence speed. The experiment results show that the improved genetic algorithm can efficiently find global optimal beyond premature convergence. Finally, using the NGA, optimizing the topology and training the weights for BP neural network are done. The results of the applications show that the new genetic algorithm is practical and efficient.
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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Dynamic Mutation Genetic Algorithm

  • 1. Dept,of Electronic Engineering,Chengdu Aeronautic Vocational & Technical College Chengdu 610061;
  • 2. College of Electronic and Information,Sichuan University Chengdu 610064
  • Received Date: 2002-01-19
  • Publish Date: 2002-06-15

Abstract: Genetic Algorithms is Optimal Algorithm, which employ a search technique based on ideas from Darwin's natural evolution theory. A new genetic algorithm (NGA) is proposed in this paper, which not only can keep the population diversity but also has quicker convergence speed. The experiment results show that the improved genetic algorithm can efficiently find global optimal beyond premature convergence. Finally, using the NGA, optimizing the topology and training the weights for BP neural network are done. The results of the applications show that the new genetic algorithm is practical and efficient.

Zhang Yu, Guo Jing, Zhou Jiliu. Dynamic Mutation Genetic Algorithm[J]. Journal of University of Electronic Science and Technology of China, 2002, 31(3): 234-239.
Citation: Zhang Yu, Guo Jing, Zhou Jiliu. Dynamic Mutation Genetic Algorithm[J]. Journal of University of Electronic Science and Technology of China, 2002, 31(3): 234-239.
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