Look-Ahead Genetic Programming for Uncertain Capacitated Arc Routing Problem

Jordan MacLachlan, Yi Mei

Abstract: Genetic Programming Hyper-Heuristic (GPHH) has been successfully applied to evolve routing policies for the Uncertain Capacitated Arc Routing Problem (UCARP). However, the current GPHH approaches have a limitation that they only consider myopic information of the current decision step. In this paper, we proposed incorporating look-ahead information to the decision process of GP-evolved routing policies. We designed a number of potentially promising chains of candidate tasks, and expand the candidate task pool to consider both the single tasks and task chains. This way, the routing policy can consider the look-ahead information incorporated in the considered task chains. The proposed GP with Chain Policies (GPCP) was compared with the standard GPHH on a range of UCARP instances, and the results showed that the task chains can improve the effectiveness of the routing policies sometimes. The better performance of a routing policy largely depends on whether it can balance the selections of single tasks and task chains, and whether it can stick to the whole selected chain rather than only the first task of the chain. In addition, there are some abnormal runs with serious overfitting issue that we will address in our future work.