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This paper proposes a data mining algorithm based on genetic algorithm and entropy for rule discovery called Genetic-Miner. The goal of Genetic-Miner is to discover classification rules in data sets. We have compared the performance of Genetic-Miner with other two well-known algorithms in six public domain data sets. The results showed that, Genetic-Miner is particularly advantageous when it is important to minimize the number of discovered rules and rule terms in order to improve comprehensibility of the discovered knowledge.