Genetic programming techniques that evolve recurrent neural network architectures for signal processing

Abstract

We propose a novel design paradigm for recurrent neural networks. This employs a two-stage genetic programming/simulated annealing hybrid algorithm to produce a neural network which satisfies a set of design constraints. The genetic programming part of the algorithm is used to evolve the general topology of the network, along with specifications for the neuronal transfer functions, while the simulated annealing component of the algorithm adapts the network's connection weights in response to a set of training data. Our approach offers important advantages over existing methods for automated network design. Firstly, it allows us to develop recurrent network connections; secondly, we are able to employ neurones with arbitrary transfer functions, and thirdly, the approach yields an efficient and easy to implement on-line training algorithm. The procedures involved in using the GP/SA hybrid algorithm are illustrated by using it to design a neural network for adaptive filtering in a signal processing application.