Extended Genetic Programming Using Apriori Algorithm for Rule Discovery

Genetic programming (GP) usually has a wide search space and can use tree structure as its chromosome expression. So, GP may search for global optimum solution. But, in general, GP's learning speed is not so fast. Apriori algorithm is one of algorithms for generation of association rules. It can be applied to large database. But, It is difficult to define its parameters without experience. We propose a rule discovery technique from a database using GP combined with association rule algorithm. It takes rules generated by the association rule algorithm as 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 meningoencephalitis diagnosis activity data in a hospital. We got domain expert's comments on our results. We discuss the result of proposed method with prior ones.