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A Multi-objective GA-based Fuzzy Modeling Approach for Constructing Pareto-optimal Fuzzy systems


Xing Zong-Yi, Hou Yuan-Long, Zhang Yong, Jia Li-Min


Vol. 6  No. 5  pp. 213-219


An approach to construct multiple Pareto-optimal fuzzy systems based on a multi-objective genetic algorithm is proposed in this paper. First, in order to obtain a good initial fuzzy system, a modified fuzzy clustering algorithm is used to identify the antecedents of fuzzy system, while the consequents are designed separately to reduce computational burden. Second, a Pareto multi-objective genetic algorithm based on NSGA-II and the interpretability- driven simplification techniques are used to evolve the initial fuzzy system iteratively with three objectives: the precision performance, the number of fuzzy rules and the number of fuzzy sets. Resultantly, multiple Pareto- optimal fuzzy systems are obtained. The proposed approach is applied to two benchmark problems, and the results show its validity.


Fuzzy modeling, fuzzy system, multi-objective genetic algorithm, Pareto-optimal, interpretability