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Efficiency Aspects of Neural Network Architecture Evolution Using Direct and Indirect Encoding

  • H. Kwasnicka
  • M. Paradowski

Abstract

Using a GA as a NN designing tool deals with many aspects. We must decide, among others, about: coding schema, evaluation function, genetic operators, genetic parameters, etc. This paper focuses on an efficiency of NN architecture evolution. We use two main approaches for neural network representation in the form of chromosomes: direct and indirect encoding. Presented research is a part of our wider study of this problem [1, 2]. We present the influence of coding schemata on the possibilities of evolving optimal neural network.

Keywords

Neural Network Genetic Algorithm Terminal Symbol Direct Encode Architecture Evolution 
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/Wien 2005

Authors and Affiliations

  • H. Kwasnicka
    • 1
  • M. Paradowski
    • 1
  1. 1.Department of Computer ScienceWroclaw University of TechnologyPoland

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