A multi-objective neuro-evolutionary algorithm to obtain interpretable fuzzy models

Authors:
Gracia Sánchez;Fernando Jiménez;José F. Sánchez;José M. Alcaraz
Affiliations:
Department of Ingeniería de la Información y las Comunicaciones, University of Murcia;Department of Ingeniería de la Información y las Comunicaciones, University of Murcia;Department of Ingeniería de la Información y las Comunicaciones, University of Murcia;Department of Ingeniería de la Información y las Comunicaciones, University of Murcia
Venue:
CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
Year:
2009

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Abstract

In this paper, a multi-objective constrained optimization model is proposed to improve interpretability of TSK fuzzy models. This approach allows a linguistic approximation of the fuzzy models. A multi-objective evolutionary algorithm is implemented with three different selection and generational replacements schemata (Niched Preselection, NSGA-II and ENORA) to generate fuzzy models in the proposed optimization context. The results clearly show a real ability and effectiveness of the proposed approach to find accurate and interpretable TSK fuzzy models. These schemata have been compared in terms of accuracy, interpretability and compactness by using three test problems studied in literature. Statistical tests have also been used with optimality and diversity multi-objective metrics to compare the schemata.