Learning of neural network parameters using a fuzzy genetic algorithm

  • Authors:

  • S. H. Ling;H. K. Lam;F. H. F. Leung;P. K. S. Tam

  • Affiliations:

  • Centre for Multimedia Signal Process., Hong Kong Polytech.Univ., Kowloon, China;Centre for Multimedia Signal Process., Hong Kong Polytech.Univ., Kowloon, China;Centre for Multimedia Signal Process., Hong Kong Polytech.Univ., Kowloon, China;Centre for Multimedia Signal Process., Hong Kong Polytech.Univ., Kowloon, China

  • Venue:
  • CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
  • Year:
  • 2002

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Abstract

This paper presents the learning of neural network parameters using a fuzzy genetic algorithm (GA). The proposed fuzzy GA is modified from the traditional GA with arithmetic crossover and non-uniform mutation. By introducing modified genetic operations, it will be shown that the performance of the proposed fuzzy GA are better than the traditional GA based on some benchmark test functions. Using the fuzzy GA, the parameters of the neural networks can be tuned. An application example on sunspot forecasting is given to show the merits of the proposed fuzzy GA.