USP Electronic Research Repository

Competitive island cooperative neuro - evolution of feedforward networks for time series prediction

Nand, Ravneil and Chandra, Rohitash (2016) Competitive island cooperative neuro - evolution of feedforward networks for time series prediction. In: Artificial Life and Computational Intelligence. Lecture Notes in Computer Science, 9592 . Springer International Publishing, Switzerland, pp. 285-297. ISBN 9783319282695

[img] PDF - Published Version
Restricted to Repository staff only

Download (386Kb)

    Abstract

    Problem decomposition, is vital in employing cooperative coevolution for neuro-evolution. Different problem decomposition methods have features that can be exploited through competition and collaboration. Competitive island cooperative coevolution (CICC) implements decomposition methods as islands that compete and collaborate at different phases of evolution. They have been used for training recurrent neural networks for time series problems. In this paper, we apply CICC for training feedforward networks for time series problems and compare their performance. The results show that the proposed approach has improved the results when compared to standalone cooperative coevolution and shows competitive results when compared to related methods from the literature.

    Item Type: Book Chapter
    Additional Information: DOI:10.1007/978-3-319-28270-1_24
    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: Ravneil Nand
    Date Deposited: 23 Aug 2016 14:13
    Last Modified: 22 Mar 2017 14:46
    URI: http://repository.usp.ac.fj/id/eprint/9183
    UNSPECIFIED

    Actions (login required)

    View Item

    Document Downloads

    More statistics for this item...