318 Int. J. Data Mining, Modelling and Management, Vol. 5, No. 4, 2013 Copyright © 2013 Inderscience Enterprises Ltd. A new memetic approach for the classification rules extraction problem Sadjia Benkhider* and Habiba Drias Department of Computer Sciences, Laboratory of Artificial Intelligence Research LRIA, University of Sciences and Technology, P.O. Box 32, El Alia, 16111 Algiers, Algeria E-mail: sadjiab@yahoo.com E-mail: hdrias@usthb.dz *Corresponding author Abstract: This paper presents a memetic algorithm applied to the classification rules extraction problem. In our new approach, our aim is to obtain a better results accuracy relatively to that obtained by a standard genetic algorithm (GA). A memetic algorithm is based on a GA which is improved by hybridising a local search approach. We made a hybrid method to compute a model of classification. In the literature, there are many hybridisation forms: in this paper, we have chosen to make our local search algorithm also based on a genetic approach so our hybridisation is purely evolutionary. Keywords: data mining; evolutionary computation; extraction of classification rules; Michigan approach; memetic approach; meta-meta hybridisation. Reference to this paper should be made as follows: Benkhider, S. and Drias, H. (2013) ‘A new memetic approach for the classification rules extraction problem’, Int. J. Data Mining, Modelling and Management, Vol. 5, No. 4, pp.318–332. Biographical notes: Sadjia Benkhider received her MSc in Computer Sciences from the University of Sciences and Technology USTHB in 2002. Since then, she is teaching several courses in the same university in the fields of computers architecture, data mining and metaheuristics. She is actually a PhD student in the KDD area and she is part of the Laboratory of Artificial Intelligence Research where her major interest researches are in the topic of classification rules extraction and evolutionary approaches. Habiba Drias is a Full Professor in the Department of Computer Sciences at the University of Sciences and Technology USTHB, where she heads the Laboratory of Artificial Intelligence Research and she is the Director of MS in Artificial Intelligence at the USTHB. She received her PhD in Computer Science in 1993 from USTHB and the University of Paris 6. She is currently teaching several courses in distributed artificial intelligence and multi-agents systems, problem solving and metaheuristics, computational complexity and compiling techniques.