Using Genetic Algorithms for Training Data Selection in RBF Networks

  • Colin R. Reeves
  • Daniel R. Bush
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 608)


The problem of generalization in the application of neural networks (NNs) to classification and regression problems has been addressed from many different viewpoints. The basic problem is well-known: minimization of an error function on a training set may lead to poor performance on data not included in the training set—a phenomenon sometimes called over-fitting.

In this paper we report on an approach that is inspired by data editing concepts in k-nearest neighbour methods, and by outlier detection in traditional statistics. The assumption is made that not all the data are equally useful in fitting the underlying (but unknown) function—in fact, some points may be positively misleading. We use a genetic algorithm (GA) to identify a ‘good’ training set for fitting radial basis function (RBF) networks, and test the methodology on two artificial classification problems, and on a real regression problem. Empirical results show that improved generalization can indeed be obtained using this approach.


Genetic algorithms radial basis functions classification regression generalization forecasting 


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Copyright information

© Springer Science+Business Media Dordrecht 2001

Authors and Affiliations

  • Colin R. Reeves
    • 1
  • Daniel R. Bush
    • 1
  1. 1.School of Mathematical and Information SciencesCoventry UniversityCoventryUK

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