A Multi-objective Evolutionary Algorithm for Tuning Fuzzy Rule-Based Systems with Measures for Preserving Interpretability

In this contribution we propose a multi-objective evolutionary algorithm for Tuning Fuzzy Rule-Based Systems by considering two objectives, accuracy and interpretability. To this aim we define a new objective that allows preserving the interpretability of the system. This new objective is an interpretability index which is the union of three metrics to preserve the original shapes of the membership functions as much as possible while a tuning of the membership function parameters is performed. The proposed method has been compared to a single objective accuracy-guided algorithm in two real problems showing that many solutions in the Pareto front dominate to those obtained by the single objective-based one.