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Self-Adaptation in Evolutionary Design of Neural Networks

  • Bohdan Macukow
  • Maciej Grzenda
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 5)

Abstract

Evolutionary computation as an alternative to the traditional methods of multilayer neural networks design has been widely applied. The results of many sim­ulations show that evolutionary algorithm can outperform standard training strate­gies, including back-propagation and its modifications.

The algorithm, described in this paper, summarises the results of our work both in the field of recurrent and feedforward networks. Its main feature is the selfadaptation procedure applied to make the search for the network weights and architeture both effective and precise.

Keywords

Learning Rule Network Weight Feedforward Network Initial Generation Standard Training 
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.

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Bohdan Macukow
    • 1
  • Maciej Grzenda
    • 1
  1. 1.Warsaw University of TechnologyWarsawPoland

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