Fuzzy Classification with Multi-objective Evolutionary Algorithms
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part I: Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira's Scientific Legacy
Information Sciences: an International Journal
A multi-objective neuro-evolutionary algorithm to obtain interpretable fuzzy models
CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
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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 hoc techniques for improving interpretability are incorporated into the multiobjective evolutionary algorithm to improve the efficiency of the algorithm. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 943–969, 2007.