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

Fischer, Manfred M. and Leung, Yee (1998) A Genetic Algorithm Based Evolutionary Computational Neural Network for Modelling Spatial Interaction Data. Discussion Papers of the Institute for Economic Geography and GIScience, 61/98. WU Vienna University of Economics and Business, Vienna.


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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)

Item Type: Paper
Divisions: Departments > Sozioökonomie > Wirtschaftsgeographie und Geoinformatik > Fischer
Depositing User: ePub Administrator
Date Deposited: 20 May 2014 10:57
Last Modified: 05 May 2018 01:45
URI: https://epub.wu.ac.at/id/eprint/4151


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