SGERD: A Steady-State Genetic Algorithm for Extracting Fuzzy Classification Rules From Data

Abstract--This paper considers the automatic design of fuzzyrule-based classification systems from labeled data. The performance of classifiers and the interpretability of generated rules are of major importance in these systems. In past research, some genetic-based algorithms have been used for the rule learning process. These genetic fuzzy systems have utilized different approaches to encode rules. In this paper, we have proposed a novel steadystate genetic algorithm to extract a compact set of good fuzzy rules from numerical data (SGERD). The selection mechanism of this algorithm is nonrandom, and only the best individuals can survive. Our approach is very simple and fast, and can be applied to high-dimensional problems with numerical attributes. To select the rules having high generalization capabilities, our algorithm makes use of some rule- and data-dependent parameters. We have also proposed an enhancing function that modifies the rule evaluation measures in order to assess the ...

• Type: Journal
• Year: 2008
• Where: TFS
• Authors: Eghbal G. Mansoori, Mansoor J. Zolghadri, Seraj D. Katebi
• Keywords: Algorithm, Fuzzyrule-based Classification Systems, Steadystate Genetic Algorithm, TFS 2008