Transfer Learning: A Building Block Selection Mechanism in Genetic Programming for Symbolic Regression

In machine learning, transfer learning is concerned with utilising prior knowledge as a way to improve the process of training a new model in a different, but related, domain. Transfer learning has been shown to be beneficial across a large set of problems. One of the main questions any transfer learning approach must address is "What to transfer?". This paper proposes a new transfer learning method in genetic programming (GP) to improve solving symbolic regression problems by extracting all potentially good and unique building blocks from a source problem. The proposed method is compared against standard GP and a state-of-the-art GP method on ten regression datasets. The experimental results show that the proposed method has achieved significantly better or comparable performance to that of the competitive methods. Furthermore, the proposed method shows better initial population and convergence compared to the other methods.