Genetic programming combined with association rule algorithm for decision tree construction

Genetic programming (GP) usually has a wide search space and a high flexibility. So, GP may search for a global optimum solution. In general, GP´s learning speed is not so fast. The Apriori Algorithm is one of the association rule algorithms. It can be applied to large databases, but it is difficult to define its parameters without experience. We propose a rule generation technique from a database using GP combined with an association rule algorithm. It takes rules generated by the association rule algorithm as the initial individual of GP. The learning speed of GP is improved by the combined algorithm. To verify the effectiveness of the proposed method, we apply it to the decision tree construction problem from the UCI Machine Learning Repository. We compare the result of proposed method with prior ones