Transfer learning based on semantic approximation techniques in genetic programming

Abstract

Transfer learning aims to reuse the knowledge taken from different but related source domains to enhance learning performance on target domains. In Genetic Programming (GP), transfer learning methods have helped GP systems expand their applicability in real applications. In this work, two transfer learning methods based on semantic approximation techniques (SAT) for GP are proposed. The proposed methods use the best individuals in the final generation of the source task as transfer material to the target task. Before transferring, SAT is applied on these individuals in order to simultaneously satisfy two objectives of reducing the size of the transferred individuals and preserving the knowledge of them. The methods are tested on ten symbolic regression datasets and compared with GP without transfer learning and two GP transfer learning methods. Experimental results showed that the proposed methods could effectively extract more useful knowledge from source domains to help to improve the performance of GP systems on the target domains.