Analysis of the Performance of a Semantic Interpretability-Based Tuning and Rule Selection of Fuzzy Rule-Based Systems by Means of a Multi-Objective Evolutionary Algorithm

Recently, a semantic interpretability index has been proposed to preserve the semantic interpretability of Fuzzy Rule-Based Systems while it tuning of the membership functions is performed In tins work, we extend the proposed multi-objective evolutionary algorithm in order to analyze the performance of the tuning based on this semantic interpretability index wink c it is combined with a lute selection To this end, the following Once objectives have been considered error and complexity minimization, and semantic interpretability maximization The analyzed method is compared to a. single objective algorithm and to the previous approach m two problems showing that many solutions in the Pareto UHL dominate to those obtained by these methods