of evolutionary multiobjective optimization (EMO). Each fuzzy rule is represented by its antecedent

www.elsevier.com/locate/ijar0888-613X/ $- see front matter 2006 Elsevier Inc. All rights reserved.fuzzy sets as an integer string of fixed length. Each fuzzy rule-based classifier, which is a set of fuzzy rules, is represented as a concatenated integer string of variable length. Our GBML algorithm simul-taneously maximizes the accuracy of rule sets and minimizes their complexity. The accuracy is mea-sured by the number of correctly classified training patterns while the complexity is measured by the number of fuzzy rules and/or the total number of antecedent conditions of fuzzy rules. We examine the interpretability-accuracy tradeoff for training patterns through computational experiments on some benchmark data sets. A clear tradeoff structure is visualized for each data set. We also examine the interpretability-accuracy tradeoff for test patterns. Due to the overfitting to training patterns, a clear tradeoff structure is not always obtained in computational experiments for test patterns. 2006 Elsevier Inc. All rights reserved