en Research Paper Research Paper 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. Intelligent Genetic Classifiers,Robust Genetic Classifiers,Fuzzy Controller,Genetic Algorithm,Optimum Decision Hyperplanes, 1 9 http://ijeee.iust.ac.ir/browse.php?a_code=A-10-3-49&slc_lang=en&sid=1 S. H. Zahiri 18003194753284600928 18003194753284600928 Yes H. Rajabi Mashhadi 18003194753284600929 18003194753284600929 No S. A. Seyedin 18003194753284600930 18003194753284600930 No