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- Publisher
- Springer Journals
- Copyright
- Copyright © 2004 by Springer-Verlag London Limited
- Subject
- Computer Science; Artificial Intelligence (incl. Robotics); Data Mining and Knowledge Discovery; Probability and Statistics in Computer Science; Computational Science and Engineering; Image Processing and Computer Vision; Computational Biology/Bioinformatics
- ISSN
- 0941-0643
- eISSN
- 1433-3058
- DOI
- 10.1007/s00521-004-0404-5
- Publisher site
- See Article on Publisher Site
Abstract
The problems associated with training feedforward artificial neural networks (ANNs) such as the multilayer perceptron (MLP) network and radial basis function (RBF) network have been well documented. The solutions to these problems have inspired a considerable amount of research, one particular area being the application of evolutionary search algorithms such as the genetic algorithm (GA). To date, the vast majority of GA solutions have been aimed at the MLP network. This paper begins with a brief overview of feedforward ANNs and GAs followed by a review of the current state of research in applying evolutionary techniques to training RBF networks.
Journal
Neural Computing and Applications – Springer Journals
Published: Apr 24, 2004