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Parallel Distributed Two-Level Evolutionary Multiobjective Methodology for Granularity Learning and Membership Functions Tuning in Linguistic Fuzzy Systems

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4 Author(s)
De Vega, M.A. ; Dept. of Inf. Technol., Univ. of Huelva, Huelva, Spain ; Bardallo, J.M. ; Marquez, F.A. ; Peregrin, A.

This paper deals with the learning of the membership functions for Mamdani fuzzy systems - the number of labels of the variables and the tuning of them - in order to obtain a set of linguistic fuzzy systems with different trade-offs between accuracy and complexity, through the use of a two-level evolutionary multi-objective algorithm. The presented methodology employs a high level main evolutionary multi-objective heuristic searching the number of labels, and some distributed low level ones, also evolutionary, tuning the membership functions of the candidate variable partitions.

Published in:

Intelligent Systems Design and Applications, 2009. ISDA '09. Ninth International Conference on

Date of Conference:

Nov. 30 2009-Dec. 2 2009