A Genetic Algorithm as a Learning Method Based on Geometric Representations

  • Gregory A. Holifield
  • Annie S. Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2724)

Abstract

A number of different methods combining the use of neural networks and genetic algorithms have been described [1]. This paper discusses an approach for training neural networks based on the geometric representation of the network. In doing so, the genetic algorithm becomes applicable as a common training method for a number of machine learning algorithms that can be similarly represented. The experiments described here were specifically derived to construct claim regions for Fuzzy ARTMAP Neural Networks [2],[3].

References

  1. 1.
    J.T. Alander. An indexed bibliography of genetic algorithms and neural networks.Google Scholar
  2. 2.
    G.A. Carpenter et al. Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of multidimensional maps. IEEE Transactions on Neural Networks, 3(5):698–713., 1992.CrossRefGoogle Scholar
  3. 3.
    Michael Georgiopoulos and Christos Christodoulou. Applications of Neural Networks in Electromagnetics. Artech House, 2001.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Gregory A. Holifield
    • 1
  • Annie S. Wu
    • 1
  1. 1.School of Electrical Engineering and Computer ScienceUniversity of Central FloridaOrlandoUSA

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