Optimization of neural network topologies using genetic algorithm

Neural networks (NN) are widely applied in modeling. The modeling process in which neural networks are applied involves the same problems as identification in general. In addition, the parameterization selected for the NN model is a key issue i.e., what number of hidden nodes is appropriate and what is their connectivity. This paper describes an evolutionary method for selecting the topology of a feed forward neural network by means of a genetic algorithm. The approach is a hybrid method utilizing a genetic algorithm for structure selection and a second-order training algorithm for parameter estimation. The method is tested with two modeling problems, one a well-known benchmark of an infra-red laser and the other modeling of a laboratory-sized pilot process imitating a paper machine head box.