Evolving fuzzy rule based controllers using genetic algorithms
Fuzzy Sets and Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Engineering and Computer Science
Genetic Algorithms in Engineering and Computer Science
Fuzzy Logic-A Modern Perspective
IEEE Transactions on Knowledge and Data Engineering
A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Implementation of evolutionary fuzzy systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Selecting fuzzy if-then rules for classification problems using genetic algorithms
IEEE Transactions on Fuzzy Systems
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In this paper, we propose a method for designing an efficient fuzzy classifier that consists of a small number of fuzzy rules with only a few antecedent fuzzy sets using a novel intelligent genetic algorithm (IGA). It is known that the number of fuzzy rules will be exploded as the number of features increases. So the fuzzy classifier with many input variables has extremely large number of fuzzy rules for high-dimensional pattern classification problems. To cope with this large rule base problem, our proposed method has the following three merits: (1) A flexible genetic parameterized fuzzy region is proposed to efficiently partition the feature space. (2) The parametric genes for representing the membership functions and fuzzy rules, and the control genes used for useful pattern feature selection and dummy fuzzy rule deletion are incorporated into a single chromosome. This means that the participated features, the membership function of each antecedent fuzzy set, and the fuzzy rules are simultaneously determined. (3) The efficient fuzzy classifier design is formulated as a large parameter optimization problem (LPOP). We solve LPOPs using a novel IGA that is superior to the conventional genetic algorithms in solving LPOPs. Computer simulations on the iris and wine classification problems illustrate the high performance of the proposed method and the simulation results are superior to those of the existing methods.