Abstract

A novel approach to construct a set of interpretable and precise fuzzy systems based on the Pareto multi-objective cooperative co-evolutionary algorithm (PMOCCA) is proposed in this paper. First, feature selection is used to reduce the dimensionality of the data in order to both improve the performance and reduce computational effort. Then the fuzzy clustering algorithm is employed to identify the initial fuzzy system. Third, the PMOCCA is carried out to evolve the initial fuzzy system to optimize the number of rules, the antecedents of the rules and the parameters of the antecedents simultaneously. In this step, the interpretability-driven simplification techniques are used iteratively to reduce the fuzzy systems, thus the interpretability of the fuzzy systems is improved. Finally, the proposed approach is applied to several benchmark problems, and the results show its validity.

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