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A Grammatical Genetic Programming Representation for Radial Basis Function Networks

  • Ian Dempsey
  • Anthony Brabazon
  • Michael O’Neill
Part of the Studies in Computational Intelligence book series (SCI, volume 82)

Summary

We present a hybrid algorithm where evolutionary computation, in the form of grammatical genetic programming, is used to generate Radial Basis Function Networks. An introduction to the underlying algorithms of the hybrid approach is outlined, followed by a description of a grammatical representation for Radial Basis Function networks. The hybrid algorithm is tested on five benchmark classification problem instances, and its performance is found to be encouraging.

Keywords

Radial Basis Function Problem Instance Hide Node Hybrid Algorithm Radial Basis Function Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Ian Dempsey
    • 1
  • Anthony Brabazon
    • 1
  • Michael O’Neill
    • 1
  1. 1.Natural Computing Research and Applications GroupUniversity College DublinIreland

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