Kizilkaya Aydogan, EmelKaraoglan, IsmailPardalos, Panos M.2020-03-262020-03-262012Kızılkaya Aydogan, E., Karaoglan, I., Pardalos, P. M., (2012). hGA: Hybrid Genetic Algorithm in Fuzzy Rule-based Classification Systems for High-dimensional Problems. Applied Soft Computing. 12(2), 800-806. doi:10.1016/j.asoc.2011.10.0101568-49461872-9681https://dx.doi.org/10.1016/j.asoc.2011.10.010https://hdl.handle.net/20.500.12395/28107The 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. Published by Elsevier B.V.en10.1016/j.asoc.2011.10.010info:eu-repo/semantics/openAccessFuzzy rule based classification systemsGenetic algorithmsGenetic fuzzy systemsClassificationInteger programmingArticle122800806Q1WOS:000298631400021Q1