Biological Cybernetics

, Volume 63, Issue 6, pp 487–493

Evolving neural networks

  • D. B. Fogel
  • L. J. Fogel
  • V. W. Porto
Article

DOI: 10.1007/BF00199581

Cite this article as:
Fogel, D.B., Fogel, L.J. & Porto, V.W. Biol. Cybern. (1990) 63: 487. doi:10.1007/BF00199581

Abstract

Neural networks are parallel processing structures that provide the capability to perform various pattern recognition tasks. A network is typically trained over a set of exemplars by adjusting the weights of the interconnections using a back propagation algorithm. This gradient search converges to locally optimal solutions which may be far removed from the global optimum. In this paper, evolutionary programming is analyzed as a technique for training a general neural network. This approach can yield faster, more efficient yet robust training procedures that accommodate arbitrary interconnections and neurons possessing additional processing capabilities.

Copyright information

© Springer-Verlag 1990

Authors and Affiliations

  • D. B. Fogel
    • 1
  • L. J. Fogel
    • 1
  • V. W. Porto
    • 1
  1. 1.ORINCON CorporationSan DiegoUSA

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