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Neuron - synapse level problem decomposition method for cooperative neuro - evolution of feedforward networks for time series prediction Prediction

Nand, Ravneil and Chandra, Rohitash (2015) Neuron - synapse level problem decomposition method for cooperative neuro - evolution of feedforward networks for time series prediction Prediction. In: Neural Information Processing. Lecture Notes in Computer Science . Springer International Publishing, Switzerland, pp. 90-100. ISBN 978-3-319-26554-4

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    Abstract

    A major concern in cooperative coevolution for neuro-evolution is the appropriate problem decomposition method that takes into account the architectural properties of the neural network. Decomposition to the synapse and neuron level has been proposed in the past that have their own strengths and limitations depending on the application problem. In this paper, a new problem decomposition method that combines neuron and synapse level is proposed for feedfoward networks and applied to time series prediction. The results show that the proposed approach has improved the results in selected benchmark data sets when compared to related methods. It also has promising performance when compared to other computational intelligence methods from the literature.

    Item Type: Book Chapter
    Additional Information: DOI:10.1007/978-3-319-26555-1_11 Paper presented at 22nd International Conference, ICONIP 2015, Istanbul, Turkey, November 9-12, 2015, Proceedings Part III.
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences
    Depositing User: Rohitash Chandra
    Date Deposited: 10 Mar 2016 15:41
    Last Modified: 10 Jun 2016 09:49
    URI: http://repository.usp.ac.fj/id/eprint/8432
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