On Generating Fuzzy Systems based on Pareto Multi-objective Cooperative Coevolutionary Algorithm

다국어 초록 (Multilingual Abstract)

An approach to construct multiple interpretable and precise fuzzy systems based on the Pareto Multi-objective Cooperative Coevolutionary Algorithm (PMOCCA) is proposed in this paper. First, a modified fuzzy clustering algorithm is used to construct antecedents of fuzzy system, and consequents are identified separately to reduce computational burden. Then, the PMOCCA and the interpretability-driven simplification techniques are executed to optimize the initial fuzzy system with three objectives: the precision performance, the number of fuzzy rules and the number of fuzzy sets; thus both the precision and the interpretability of the fuzzy systems are improved. In order to select the best individuals from each species, we generalize the NSGA-Ⅱ algorithm from one species to multi-species, and propose a new non-dominated sorting technique and collaboration mechanism for cooperative coevolutionary algorithm. Finally, the proposed approach is applied to two benchmark problems, and the results show its validity.

목차 (Table of Contents)

• Abstract
• 1. INTRODUCTION
• 2. PRELIMINARIES
• 3. CONSTRUCTON OF AN INITIAL FUZZY SYSTEM
• 4. INTERPRETABILITY-DRIVENSIMPLIFICATION TECHNIQUES
• 5. PARETO MULTI-OBJECTIVE COOPERATIVE COEVOLUTIONARY ALGORITHM (PMOCCA)
• 6. EXPERIMENTS AND RESULTS
• 7. CONCLUSIONS
• REFERENCES

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