Genetic optimisation of control parameters of a neural network

Abstract

One of the shortcomings of artificial neural networks (ANNs) is the difficulty in predicting the best control parameters for a certain application. The number of combinations of parameters is very large. This makes it very inefficient and expensive to search manually by trial and error. Genetic algorithms (GAs) are an excellent and effective search technique suitable for this task. This paper describes an investigation into the use of GAs to automate the choice of parameters in both a standard backpropagation (SBP) and a fuzzy backpropagation (FBP) network for different applications.