Emergence of learning rule in neural networks using genetic programming combined with decision trees

In this paper, genetic programming (GP) combined with decision trees is used to evolve the structure and weights for artificial neural network (ANN). The learning rule of the decision tree technique is defined as a function of global information by employing a divide-and-conquer strategy. Learning rules with lower fitness values are replaced by new ones generated using GP techniques. The reciprocal connection between decision tree and GP emerges from the coordination of learning rules. Since there is no constraint on initial network structure, a more suitable network can be found for a given task. Further, the fitness values are improved by using a hybrid GP technique which is a combined technique of GP and back propagation. The proposed method is applied to a medical diagnostic system and experimental results demonstrate that effective learning rule evolves.