The initiative of combining association rule mining with fuzzy set theory has been applied frequently in recent years1–5. The original idea comes from dealing with quantitative attributes in a database, where discretization of the quantitative values into intervals would lead to under or overestimation of the values that are near the borders. This is called the sharp boundary problem. Fuzzy sets can help us to overcome this problem by allowing different degrees of the membership, not only 1 and 0 treated by traditional methods. Attribute values can thereby be the members of more than one set and therefore give a more realistic view on such data. On the other hand, fuzzy set theory has been shown to be a very useful tool in association rule mining, because the mined rules can be expressed in linguistic terms, which are more natural and understandable for human beings. The linguistic representation is mainly useful when those discovered rules are presented to human experts for study. In this paper, a novel association rule mining approach that integrates the evolutionary optimization technique ‘genetic network programming (GNP)’ and fuzzy set theory has been proposed for mining interesting fuzzy rules from given quantitative data. The performance of our algorithm has been compared with other relevant algorithms and the experimental results show the advantages and effectiveness of the proposed model. Copyright © 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.