Genetic generation of high-degree-of-freedom feed-forward neural networks

Abstract

Appropriate definition of neural network architecture prior to data analysis is crucial for successful data mining. This is particularly important when the underlying model of the data is unknown. The proposed algorithm is intended to develop automatically an appropriate neural network (including the number of layers, the number of processing elements per layer, and types of each processing element) needed to solve the given problem. Genetic programming (GP) is used to develop the neural network's structure and the resilientback- propagation (RPROP) will be used to train the neural network. © Springer-Verlag 2004.