This paper describes a new approach, HIerarchical DEcision Rules (HIDER), for learning generalizable rules in continuous and discrete domains based on evolutionary algorithms. The main contributions of our approach are the integration of both binary and real evolutionary coding; the use of specific operators; the relaxing coefficient to construct more flexible classifiers by indicating how general, with respect to the errors, decision rules must be; the coverage factor in the fitness function, which makes possible a quick expansion of the rule size; and the implicit hierarchy when rules are being obtained. HIDER is accuracy-aware since it can control the maximum allowed error for each decision rule. We have tested our system on real data from the UCI Repository. The results of a 10-fold cross-validation are compared to C4.5's and they show a significant improvement with respect to the number of rules and the error rate. © 2003 Elsevier Inc. All rights reserved.