A Three-Objective Evolutionary Approach to Generate Mamdani Fuzzy Rule-Based Systems

Abstract. In the last years, several papers have proposed to adopt multiobjective evolutionary algorithms (MOEAs) to generate Mamdani fuzzy rulebased systems with different trade-offs between interpretability and accuracy. Since interpretability is difficult to quantify because of its qualitative nature, several measures have been introduced, but there is no general agreement on any of them. In this paper, we propose an MOEA to learn concurrently rule base and membership function parameters by optimizing accuracy and interpretability, which is measured in terms of number of conditions in the antecedents of rules and partition integrity. Partition integrity is evaluated by using a purposely-defined index based on the piecewise linear transformation exploited to learn membership function parameters. Results on a real-world regression problem are shown and discussed.

• Type: Journal
• Year: 2009
• Where: HAIS
• Authors: Michela Antonelli, Pietro Ducange, Beatrice Lazzerini, Francesco Marcelloni
• Keywords: Artificial Intelligence, HAIS 2009, Interpretability, Membership Function Parameters, Partition Integrity