A single-objective genetic-fuzzy approach for multi-objective fuzzy problems

In this paper, a genetic algorithm-based search method, which builds ideal rule set for fuzzy rule-based classification systems (FRBCSs), is developed. In FRBCSs, ideal rule set means a set of rules which ensure high classification accuracy with small rule count and small rule length. The related studies in the literature point out that rule set grows exponentially with input attribute count. This growth complicates the searching process and lowers the success rate. Through the proposed method, successive results are obtained for datasets with large input attribute counts using a special coding technique. The proposed method is tested for various datasets and results are compared against the method which uses candidate rule set.