Interactive Fuzzy Modeling by Evolutionary Multiobjective Optimization with User Preference

One of the new trends in genetic fuzzy systems (GFS) is the use of evolutionary multiobjective optimization (EMO) algorithms. This is because EMO algorithms can easily handle two conflicting objectives (i.e., accuracy maximization and complexity minimization) when we design accurate and compact fuzzy rule-based systems from numerical data. Since the main advantage of fuzzy rule-based systems compared with other non-linear ones is their linguistic interpretability, the design of fuzzy rule-based systems can be viewed as linguistic data mining from numerical data. From the data mining point of view, the required knowledge strongly depends on its user. That is, the interpretability of fuzzy rule-based systems should be evaluated by taking into account the user’s preference. Although there exist a number of interpretability measures in the literature, users usually do not know which measure represents their preference beforehand. In this paper, we propose interactive fuzzy modeling by evolutionary multiobjective optimization with user’s preference. User’s preference is represented by several satisfaction level functions which can be interactively modified by the user. The user’s preference is used as one of multiple objectives in an EMO algorithm. As a case study, we apply our approach to real world time-series data of land price movements in Japan and demonstrate a user interface of our approach. Keywords— evolutionary multiobjective optimization, fuzzy modelling, interactive evolutionary computation, user preference.