A Multi-objective Cooperative Coevolutionary Algorithm for Constructing Accurate and Interpretable Fuzzy systems
Xing Zong-Yi, Hou Yuanlong, Zhang Yong, Jia Li-Min, Hou Yuexian · 2006
A novel approach to construct accurate and interpretable fuzzy classification system based on the multi-objective cooperative coevolutionary algorithm (MOCCA) 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 MOCCA with three species is carried out to evolve the initial fuzzy system to optimize its structures and parameters. In MOCCA, the interpretability-driven simplification techniques are used to reduce the fuzzy system, thus the interpretability of the fuzzy system is improved; the number of rules, the antecedents of the fuzzy rules and the parameters of the antecedents are optimized simultaneously. Finally, the proposed approach is applied to six benchmark problems, and the results show its validity.