Genetic programming hyper-heuristic with knowledge transfer for uncertain capacitated arc routing problem

The Uncertain Capacitated Arc Routing Problem (UCARP) is an important combinatorial optimisation problem. Genetic Programming (GP) has shown effectiveness in automatically evolving routing policies to handle the uncertain environment in UCARP. However, when the scenario changes, the current routing policy can no longer work effectively, and one has to retrain a new policy for the new scenario which is time consuming. On the other hand, knowledge from solving the previous similar scenarios may be helpful in improving the efficiency of the retraining process. In this paper, we propose different knowledge transfer methods from a source scenario to a similar target scenario and examine them in different settings. The experimental results showed that by knowledge transfer, the retraining process is made more efficient and the same performance can be obtained within a much shorter time without having any negative transfer.