A Genetic Algorithm Based Evolutionary Computational Neural Network for Modelling Spatial Interaction Data

    Publication: Working/Discussion PaperWU Working Paper

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    Building a feedforward computational neural network model (CNN) involves two distinct
    tasks: determination of the network topology and weight estimation. The specification of a
    problem adequate network topology is a key issue and the primary focus of this contribution.
    Up to now, this issue has been either completely neglected in spatial application domains, or
    tackled by search heuristics (see Fischer and Gopal 1994). With the view of modelling
    interactions over geographic space, this paper considers this problem as a global
    optimization problem and proposes a novel approach that embeds backpropagation learning
    into the evolutionary paradigm of genetic algorithms. This is accomplished by interweaving a
    genetic search for finding an optimal CNN topology with gradient-based backpropagation
    learning for determining the network parameters. Thus, the model builder will be relieved of
    the burden of identifying appropriate CNN-topologies that will allow a problem to be solved
    with simple, but powerful learning mechanisms, such as backpropagation of gradient descent
    errors. The approach has been applied to the family of three inputs, single hidden layer,
    single output feedforward CNN models using interregional telecommunication traffic data for
    Austria, to illustrate its performance and to evaluate its robustness. (authors' abstract)
    Original languageEnglish
    Place of PublicationVienna
    PublisherWU Vienna University of Economics and Business
    Publication statusPublished - 1 Feb 1998

    Publication series

    SeriesDiscussion Papers of the Institute for Economic Geography and GIScience

    WU Working Paper Series

    • Discussion Papers of the Institute for Economic Geography and GIScience

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