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The extraction of comprehensible knowledge is one of the major challenges in many domains. In this paper, an ant programming (AP) framework, which is capable of mining classification rules easily comprehensible by humans, and, therefore, capable of supporting expert-domain decisions, is presented. The algorithm proposed, called grammar based ant programming (GBAP), is the first AP algorithm developed for the extraction of classification rules, and it is guided by a context-free grammar that ensures the creation of new valid individuals. To compute the transition probability of each available movement, this new model introduces the use of two complementary heuristic functions, instead of just one, as typical ant-based algorithms do. The selection of a consequent for each rule mined and the selection of the rules that make up the classifier are based on the use of a niching approach. The performance of GBAP is compared against other classification techniques on 18 varied data sets. Experimental results show that our approach produces comprehensible rules and competitive or better accuracy values than those achieved by the other classification algorithms compared with it.