Transfer learning is a machine learning technique which has demonstrated great success in improving outcomes on a broad range of problems. However prior methods of transfer learning in Genetic Programming (GP) have tended to rely on random processes or meta-knowledge of the problem structure to facilitate selection of information for use in transfer. To address these issues, a non-random method for automatically finding relevant information for transfer between two source domain problems from the same problem domain based on common subtrees is proposed. This information is then utilised within a modular transfer learning framework, being added to the function set for a target problem prior to population initialisation. The performance of the proposed method is assessed using multiple benchmark problems from two distinct problem domains, namely symbolic regression and Boolean domain problems, and compared to standard GP and the-state-of-the-art transfer learning method for the given problems. The results show that the newly introduced method has either significantly outperformed, or achieved comparable performance to, the competitor methods on the problems of the two domains. We conclude that the proposed method demonstrates ability as a general transfer learning technique for GP and note some possible avenues for future research based off these results.
