Parameter Estimation in Neural Spatial Interaction Modelling by a Derivative Free Global Optimization Method

Manfred M. Fischer and Martin Reismann
Department of Economic and Social Geography, Wirtschaftsuniversitaet Wien,
Augasse 2-6, A-1090 Vienna, Austria
E-mail: Manfred.M.Fischer@wu-wien.ac.at
E-mail: reismann@wigeo1.wu-wien.ac.at

Katerina Hlavácková-Schindler
Institute of Computer Science, Academy of Sciences of the Czech Republic,
Pod Vodárenskou vezí 2, 18207 Praha 8, Czech Republic
E-mail: katka@uivt.cas.cz

Key Words: neural spatial interaction modelling, parameter optimization, evolutionary computation, backpropagation of conjugate gradient descent errors, real world application performance test

Parameter estimation is one of the central issues in neural spatial interaction modelling. Current practice is dominated by gradient-based local minimization techniques. They find local minima efficiently and work best in unimodal minimization problems, but can get trapped in multimodal problems. Global search procedures provide an alternative optimization scheme that allows escape from local minima. Differential evolution recently has been introduced as an efficient direct search method for optimizing real-valued multi-modal objective functions (Storn and Price 1997). The method is conceptually simple and attractive, but little is known about its behaviour in real-world applications. This paper explores this method as an alternative to current practice for solving the parameter estimation task, and attempts to assess its robustness, measured in terms of in-sample and out-of-sample performance. A benchmark comparison against back propagation of conjugate gradients is based on Austrian interregional telecommunication traffic data.