Designing Application-Specific Neural Networks Using the Genetic Algorithm

We present a general and systematic method for neural network design based on the genetic algorithm. The technique works in conjunction with network learning rules, addressing aspects of the network's gross architecture, connectivity, and learning rule parameters. Networks can be optimiled for various application(cid:173) specific criteria, such as learning speed, generalilation, robustness and connectivity. We describe a prototype system, NeuroGENESYS, that employs the backpropagation learning rule. Experiments on several small problems have been conducted.