Transfer Learning in Genetic Programming Hyper-heuristic for Solving Uncertain Capacitated Arc Routing Problem

Uncertain Capacitated Arc Routing Problem (UCARP) is a combinatorial optimization problem that has many important real-world applications. Genetic programming (GP) is a powerful machine learning technique that has been successfully used to automatically evolve routing policies for UCARP. Generalisation is an open issue in the field of UCARP and in this direction, an open challenge is the case of changes in number of vehicles which currently leads to new training procedures to be initiated. Considering the expensive training cost of evolving routing policies for UCARP, a promising strategy is to learn and reuse knowledge from a previous problem solving process to improve the effectiveness and efficiency of solving a new related problem, i.e. transfer learning. Since none of the existing GP transfer methods have been used as a hyper-heuristic in solving UCARP, we conduct a comprehensive study to investigate the behaviour of the existing GP transfer methods for evolving routing policy in UCARP, and identify the potentials of existing methods. The results suggest that the existing methods applying subtree transfer cannot scale well to environment changes and cannot be adapted for this purpose. However, applying GP transfer methods is a good option for creating a better initial populations on target domain and though this effect does not last, we can obtain comparable results in the target domain in a much shorter time. Overall, we conclude that UCARP needs stronger and more effective transfer learning methods.