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On the evolution of neural networks for pairwise classification using gene expression programming

Published: 08 July 2009 Publication History

Abstract

Neural networks are a common choice for solving classification problems, but require experimental adjustments of the topology, weights and thresholds to be effective. Success has been seen in the development of neural networks with evolutionary algorithms, making the extension of this work to classification problems a logical step. This paper presents the first known use of the Gene Expression Programming-based GEP-NN algorithm to design neural networks for classification purposes. The system uses pairwise decomposition to produce a series of binary classifiers for a given multi-class problem, with the results of the classifier set being combined by majority vote.

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    cover image ACM Conferences
    GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
    July 2009
    2036 pages
    ISBN:9781605583259
    DOI:10.1145/1569901

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 08 July 2009

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    Author Tags

    1. classification
    2. gene expression programming
    3. neural networks
    4. neuroevolution

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    GECCO09
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    GECCO09: Genetic and Evolutionary Computation Conference
    July 8 - 12, 2009
    Québec, Montreal, Canada

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