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Designing neural networks by a combination of structural learning and genetic algorithms

  • Part III: Learning: Theory and Algorithms
  • Conference paper
  • First Online:
Artificial Neural Networks — ICANN'97 (ICANN 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1327))

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Abstract

Kitano proposed to use GA with graph encoding method to have good scalability for hierarchical networks. However, this is not applicable to recurrent networks. The authors propose to use a combination of a structural learning with forgetting(SLF) and GA for designing recurrent neural networks; the former generates quasi-optimal recurrent network structure and the latter prevents local minima by global search. Its applications to two kinds of time series data well demonstrate the superiority to SLF and to a combination of GA and BPTT.

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References

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Authors and Affiliations

  1. Kyushu Institute of Technology, 820, Iizuka, Fukuoka, Japan

    Masumi Ishikawa & Kazuhiko Nishino

Authors
  1. Masumi Ishikawa
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  2. Kazuhiko Nishino
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Editor information

Wulfram Gerstner Alain Germond Martin Hasler Jean-Daniel Nicoud

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© 1997 Springer-Verlag Berlin Heidelberg

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Ishikawa, M., Nishino, K. (1997). Designing neural networks by a combination of structural learning and genetic algorithms. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020190

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  • DOI: https://doi.org/10.1007/BFb0020190

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63631-1

  • Online ISBN: 978-3-540-69620-9

  • eBook Packages: Springer Book Archive

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