A Genetic Algorithm and neural network hybrid classification scheme

This paper describes an approach to the classification of signals, based on the integration of Genetic Algorithms with Neural Network learning algorithms. First, a Genetic Algorithm is applied to the off-line task of selecting a minimal feature set, for later use in the classifier system. The second use of the Genetic Algorithm, in conjunction with the Backpropagation algorithm, is improvement in the training of multi-layer network weights. We demonstrate the effectiveness of this approach on a complex target classification task, and the promising result is generally applicable to a wide spectrum of signal classification tasks. By integrating the Genetic Algorithm with various Neural Network algorithms, we demonstrate a high degree of synergy which allows improved learning and performance, in both the extraction of key data features, and the classification of individuals based on these features.