hGA: Hybrid genetic algorithm in fuzzy rule-based classification systems for high-dimensional problems

Authors:
Emel Kızılkaya Aydogan;Ismail Karaoglan;Panos M. Pardalos
Affiliations:
Department of Industrial Engineering, Faculty of Engineering, Erciyes University, Kayseri, Turkey;Department of Industrial Engineering, Faculty of Engineering, Selcuk University, Konya, Turkey;Department of Industrial and System Engineering, Faculty of Engineering, University of Florida, Gainesville, United States and Laboratory of Algorithms and Technologies for Networks Analysis (LATN ...
Venue:
Applied Soft Computing
Year:
2012

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Abstract

The aim of this work is to propose a hybrid heuristic approach (called hGA) based on genetic algorithm (GA) and integer-programming formulation (IPF) to solve high dimensional classification problems in linguistic fuzzy rule-based classification systems. In this algorithm, each chromosome represents a rule for specified class, GA is used for producing several rules for each class, and finally IPF is used for selection of rules from a pool of rules, which are obtained by GA. The proposed algorithm is experimentally evaluated by the use of non-parametric statistical tests on seventeen classification benchmark data sets. Results of the comparative study show that hGA is able to discover accurate and concise classification rules.