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

Authors:
María José Gacto;Rafael Alcalá;Francisco Herrera
Affiliations:
Dept. Computer Science, University of Jaén, Jaén, Spain and Dept. Computer Science and A.I., University of Granada, Granada, Spain;Dept. Computer Science, University of Jaén, Jaén, Spain and Dept. Computer Science and A.I., University of Granada, Granada, Spain;Dept. Computer Science, University of Jaén, Jaén, Spain and Dept. Computer Science and A.I., University of Granada, Granada, Spain
Venue:
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Year:
2010

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Abstract

Recently, a semantic interpretability index has been proposed to preserve the semantic interpretability of Fuzzy Rule-Based Systems while a tuning of the membership functions is performed. In this work, we extend the proposed multi-objective evolutionary algorithm in order to analyze the performance of the tuning based on this semantic interpretability index while it is combined with a rule selection. To this end, the following three 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 in two problems showing that many solutions in the Pareto front dominate to those obtained by these methods.