Intelligent and Robust Genetic Algorithm Based Classifier

The concepts of robust classification and intelligently controlling the search process of genetic algorithm (ga) are introduced and integrated with a conventional genetic classifier for development of a new version of it, which is called intelligent and robust ga-classifier (irga-classifier). it can efficiently approximate the decision hyperplanes in the feature space.it is shown experimentally that the proposed irga-classifier has removed two important weak points of the conventional ga-classifiers. these problems are the large number of training points and the large number of iterations to achieve a comparable performance with the bayes classifier, which is an optimal conventional classifier.three examples have been chosen to compare the performance of designed irga-classifier to conventional ga-classifier and bayes classifier. they are the iris data classification, the wine data classification, and radar targets classification from backscattered signals. the results show clearly a considerable improvement for the performance of irga-classifier compared with a conventional ga-classifier