On the Transfer Learning of Genetic Programming Classification Algorithms

Abstract

Data classification is a real-world problem that is encountered daily in various problem domains. Genetic programming (GP) has proved to be one of the most versatile algorithms leading to its popularity as a classification algorithm. However, due to its large number of parameters, the manual design process of GP is considered to be a time consuming tedious task. As a result, there have been initiatives by the machine learning community to automate the design of GP classification algorithms. In this paper, we propose the transfer of the design knowledge gained from the automated design of GP classification algorithms from a specific source domain and apply it to design GP classification algorithms for a target domain. The results of the experiments demonstrate that the proposed approach is capable of evolving classifiers that achieve results that are competitive when compared to automated designed classifiers and better than manually tuned parameter classifiers. To the best of our knowledge, this is the first study that examines transfer learning in automated design. The proposed approach is shown to achieve positive transfer.