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Memetic cooperative neuro-evolution for chaotic time series prediction

Chandra, Rohitash and Wong , Gary and Sharma, Anuraganand (2016) Memetic cooperative neuro-evolution for chaotic time series prediction. In: Neural Information Processing. Lecture Notes in Computer Science . Springer International Publishing, Japan, pp. 299-308. ISBN 978-3-319-46674-3

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    Abstract

    Cooperative neuro-evolution has shown to be promising for chaotic time series problem as it provides global search features using evolutionary algorithms. Back-propagation features gradient descent as a local search method that has the ability to give competing results. A synergy between the methods is needed in order to exploit their features and achieve better performance. Memetic algorithms incorporate local search methods for enhancing the balance between diversification and intensification. We present a memetic cooperative neuro-evolution method that features gradient descent for chaotic time series prediction. The results show that the proposed method utilizes lower computational costs while achieving higher prediction accuracy when compared to related methods. In comparison to related methods from the literature, the proposed method has favorable results for highly noisy and chaotic time series problems.

    Item Type: Book Chapter
    Uncontrolled Keywords: Memetic algorithms Cooperative neuro-evolution Backpropagation Gradient descent Feedforward networks
    Subjects: T Technology > TJ Mechanical engineering and machinery
    Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences
    Depositing User: Repo Editor
    Date Deposited: 18 Oct 2016 10:41
    Last Modified: 18 Oct 2016 10:41
    URI: http://repository.usp.ac.fj/id/eprint/9389
    UNSPECIFIED

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