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Evolving Efficient Connection for the Design of Artificial Neural Networks

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Book cover Artificial Neural Networks - ICANN 2008 (ICANN 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5164))

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Abstract

Most of the recent neuroevolution (NE) approaches explore new network topologies based on a neuron-centered design principle. So far evolving connections has been poorly explored. In this paper, we propose a novel NE algorithm called Evolving Efficient Connections (EEC), where the connection weights and the connection paths of networks are evolved separately. We compare our new method with standard NE and several popular NE algorithms, SANE, ESP and NEAT. The experimental results indicate evolving connection weights along with connection paths can significantly enhance the performance of standard NE. Moreover the performances of cooperative coevolutionary algorithms are superior to non-cooperative evolutionary algorithms.

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Author information

Authors and Affiliations

  1. Department of Computer and Information Science, Norwegian University of Science and Technology, Norway

    Min Shi

  2. Department of Engineering Cybernetics, Norwegian University of Science and Technology, Norway

    Haifeng Wu

Authors
  1. Min Shi
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  2. Haifeng Wu
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Editor information

Véra Kůrková Roman Neruda Jan Koutník

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© 2008 Springer-Verlag Berlin Heidelberg

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Shi, M., Wu, H. (2008). Evolving Efficient Connection for the Design of Artificial Neural Networks. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87559-8_94

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  • DOI: https://doi.org/10.1007/978-3-540-87559-8_94

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87558-1

  • Online ISBN: 978-3-540-87559-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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