Evolutionary multiobjective design of fuzzy rule-based systems

The main advantage of fuzzy rule-based systems over other non-linear models such as neural networks is their high interpretability. Fuzzy rules can be usually interpreted in a linguistic manner because they are described by linguistic values such as small and large. Fuzzy rule-based systems have high accuracy as well as high interpretability. A large number of tuning methods have been proposed to improve their accuracy. Most of those tuning methods are based on learning algorithms of neural networks and/or evolutionary optimization techniques. Accuracy improvement of fuzzy rule-based systems, however, is usually achieved at the cost of interpretability. This is because the accuracy improvement often increases the complexity of fuzzy rute-based systems. Thus one important issue in the design of fuzzy rule-based systems is to find a good tradeoff between the accuracy and the complexity. The importance of finding a good accuracy-complexity tradeoff has been pointed out in some studies in the late 1990s. Recently evolutionary multiobjective optimization algorithms were used to search for various fuzzy rule-based systems with different accuracy-complexity tradeoffs. Users are supposed to choose a final model based on their preference from the obtained fuzzy rule-based systems. Some users may prefer a simple one with high interpretability. Other users may prefer a complicated one with high accuracy. In this paper, we explain evolutionary multiobjective approaches to the design of accurate and interpretable fuzzy rule-based systems. We also suggest some future research directions related to the evolutionary multiobjective design of fuzzy rule-based systems.