Abstract

A genetic programming method is investigated for optimizing both the architecture and the connection weights of multilayer feedforward neural networks. The genotype of each network is represented as a tree whose depth and width are dynamically adapted to the particular application by specifically defined genetic operators. The weights are trained by a next-ascent hillclimbing search. A new fitness function is proposed that quantifies the principle of Occam's razor. It makes an optimal trade-off between the error fitting ability and the parsimony of the network. We discuss the results for two problems of differing complexity and study the convergence and scaling properties of the algorithm. 1 INTRODUCTION Optimization of neural network architectures for particular applications is important because the speed and accuracy of learning and performance are dependent on the network complexity, i.e. the type and number of units and connections, and the connectivity of units. Fo...

Citations