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Comparison of Genetic Algorithm and Particle Swarm Optimizer When Evolving a Recurrent Neural Network

  • Matthew Settles
  • Brandon Rodebaugh
  • Terence Soule
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2723)

Abstract

This paper compares the performance of GAs and PSOs in evolving weights of a recurrent neural network. The algorithms are tested on multiple network topologies. Both algorithms produce successful networks. The GA is more successful evolving larger networks and the PSO is more successful on smaller networks.

References

  1. 1.
    Shepherd, G.M.: Neurobiology. Oxford University Press, New York, NY (1994)Google Scholar
  2. 2.
    Angeline, P.J., Saunders, G.M., Pollack, J.P.: An evolutionary algorithm that constructs recurrent neural networks. IEEE Transactions on Neural Networks 5 (1994) 54–65CrossRefGoogle Scholar
  3. 3.
    Kennedy, J., Eberhart, R.: Swarm Intelligence. Morgan Kaufmann Publishers, Inc., San Francisco, CA (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Matthew Settles
    • 1
  • Brandon Rodebaugh
    • 1
  • Terence Soule
    • 1
  1. 1.Department of Computer ScienceUniversity of IdahoMoscowUSA

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