Improving interpretability in approximative fuzzy models via multiobjective evolutionary algorithms

Current research lines in fuzzy modeling mostly tackle improving the accuracy in descriptive models and improving of the interpretability in approximative models. This article deals with the second issue, approaching the problem by means of multiobjective optimization in which accuracy and interpretability criteria are simultaneously considered. Evolutionary algorithms are especially appropriated for multiobjective optimization because they can capture multiple Pareto solutions in a single run of the algorithm. We propose a multiobjective evolutionary algorithm to find multiple Pareto solutions (fuzzy models) showing a trade-off between accuracy and interpretability. Additionally, neural-network-based techniques in combination with ad hoe techniques for improving interpretability are incorporated into the multiobjective evolutionary algorithm to improve the efficiency of the algorithm. (C) 2007 Wiley Periodicals, Inc.