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This paper presents an evolutionary algorithm using G3P (Grammar Guided Genetic Programming) for mining association rules in different real-world databases This algorithm, called G3PARM, uses an auxiliary population made up of its best individuals that will then act as parents for the next generation The individuals are defined through a context-free grammar and it allows us to obtain datatype-generic and valid individuals We compare our approach to Apriori and FP-Growth algorithms and demonstrate that our proposal obtains rules with better support, confidence and coverage of the dataset instances Finally, a preliminary study is also introduced to compare the scalability of our algorithm Our experimental studies illustrate that this approach is highly promising for discovering association rules in databases.