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Australasian Conference on Artificial Life and Computational Intelligence

ACALCI 2016: Artificial Life and Computational Intelligence pp 171–182Cite as

Reverse Neuron Level Decomposition for Cooperative Neuro-Evolution of Feedforward Networks for Time Series Prediction

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9592))

Abstract

A major challenge in cooperative neuro-evolution is to find an efficient problem decomposition that takes into account architectural properties of the neural network and the training problem. In the past, neuron and synapse Level decomposition methods have shown promising results for time series problems, howsoever, the search for the optimal method remains. In this paper, a problem decomposition method, that is based on neuron level decomposition is proposed that features a reverse encoding scheme. It is used for training feedforward networks for time series prediction. The results show that the proposed method has improved performance when compared to related problem decomposition methods and shows competitive results when compared to related methods in the literature.

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Authors and Affiliations

  1. School of Computing Information and Mathematical Sciences, University of South Pacific, Suva, Fiji

    Ravneil Nand & Rohitash Chandra

Authors
  1. Ravneil Nand
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  2. Rohitash Chandra
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Corresponding author

Correspondence to Ravneil Nand .

Editor information

Editors and Affiliations

  1. Canberra, Aust Capital Terr, Australia

    Tapabrata Ray

  2. University of New South Wales, Canberra, Aust Capital Terr, Australia

    Ruhul Sarker

  3. RMIT University, Melbourne, Victoria, Australia

    Xiaodong Li

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© 2016 Springer International Publishing Switzerland

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Nand, R., Chandra, R. (2016). Reverse Neuron Level Decomposition for Cooperative Neuro-Evolution of Feedforward Networks for Time Series Prediction. In: Ray, T., Sarker, R., Li, X. (eds) Artificial Life and Computational Intelligence. ACALCI 2016. Lecture Notes in Computer Science(), vol 9592. Springer, Cham. https://doi.org/10.1007/978-3-319-28270-1_15

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  • DOI: https://doi.org/10.1007/978-3-319-28270-1_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28269-5

  • Online ISBN: 978-3-319-28270-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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