Evolution versus training: an investigation into combining genetic algorithms and neural networks

Genetic algorithms (GAs) have been utilised as tools in neural network development across a wide range of problem domains. However a potential disadvantage with combining these two techniques is the amount of processing time taken. Three important factors which significantly affect the time taken are the GA's population size, the number of GA generations, and the number of neural network passes. To investigate the tradeoffs between the three variables, an exhaustive set of experiments were carried out on four well known classification problems. In order to evaluate the results obtained, a comparison was made with previously published results using a number of other classification techniques including backpropagation neural networks, C4.5 and 1R. Results showed that a small GA population size was favoured for all problems investigated, while the best number of GA generations and neural network passes were problem dependent.