February 26, 2010 9:15 WSPC/S1469-0268 157-IJCIA 00276 International Journal of Computational Intelligence and Applications Vol. 9, No. 1 (2010) 49–67 c  Imperial College Press DOI: 10.1142/S1469026810002768 MIXED GENETIC ALGORITHM APPROACH FOR FUZZY CLASSIFIER DESIGN D. DEVARAJ Professor and Head, Department of Electrical and Electronics Engineering Arulmigu Kalasalingam College of Engineering Krishnankoil-626190, Tamil Nadu, India deva230@yahoo.com P. GANESH KUMAR Lecturer, Department of Information Technology Anna University Coimbatore, Coimbatore 641047, Tamilnadu, India pganeshkumar ms@yahoo.co.in Received 23 December 2008 Revised 16 July 2009 An important issue in the design of FRBS is the formation of fuzzy if-then rules and the membership functions. This paper presents a Mixed Genetic Algorithm (MGA) approach to obtain the optimal rule set and the membership function of the fuzzy classifier. While applying genetic algorithm for fuzzy classifier design, the membership functions are rep- resented as real numbers and the fuzzy rules are represented as binary string. Modified forms of crossover and mutation operators are proposed to deal with the mixed string. The proposed genetic operators help to improve the convergence of GA and accuracy of the classifier. The performance of the proposed approach is evaluated through devel- opment of fuzzy classifier for seven standard data sets. From the simulation study it is found that the proposed algorithm produces a fuzzy classifier with minimum number of rules and high classification accuracy. Statistical analysis of the test results shows the superiority of the proposed algorithm over the existing methods. Keywords : Fuzzy classifier; if-then rules; membership function; genetic algorithm. 1. Introduction Fuzzy Rule Based Systems (FRBS) have been successfully applied in modeling, 9,32,43 control, 42,32 and classification problems. 27 The key to success of the FRBS is its ability to incorporate human expert knowledge in decision-making. Formation of fuzzy if-then rules and membership functions are the important tasks in the design of FRBS. Generally, the rules and membership functions are formed from the experience of human experts. But, for the problems with many input vari- ables, the possible number of rules increases exponentially, which makes it difficult for experts to define a complete rule set. Data-driven approaches 30,34,47 have been proposed for developing the FRBS from numerical data without the knowledge of domain experts. Abe et al. 1,2 49