Approach of Fuzzy Classification Based on Hybrid Co-Evolution Algorithm

A novel approach to construct accurate and interpretable fuzzy classification system based on hybrid co-evolution algorithm is proposed in this paper. First, the necessary conditions of interpretability are analyzed. Second, the search ability of Michigan-style and Pittsburgh-style genetic algorithm is examined respectively for designing fuzzy classification system. It is clearly demonstrated that each algorithm has its own advantages and disadvantages. We combine these two algorithms into a single hybrid co-evolution algorithm. The hybrid co-evolution algorithm owns three species including the number of fuzzy rules species, the premise structure species and the parameters species. Considering both precision and interpretability, the fitness function is calculated on cooperation of individuals from the three species. The proposed approach is applied to several benchmark problems, and the results show its validity.