Novel Ensemble Genetic Programming Hyper-Heuristics for Uncertain Capacitated Arc Routing Problem

The Uncertain Capacitated Arc Routing Problem (UCARP) is an important problem with many real-world applications. A major challenge in UCARP is to handle the uncertain environment effectively and reduce the recourse cost upon route failures. Genetic Programming Hyper-heuristic (GPHH) has been successfully applied to automatically evolve effective routing policies to make real-time decisions in the routing process. However, most existing studies obtain a single complex routing policy which is hard to interpret. In this paper, we aim to evolve an ensemble of simpler and more interpretable routing policies than a single complex policy. By considering the two critical properties of ensemble learning, i.e., the effectiveness of each ensemble element and the diversity between them, we propose two novel ensemble GP approaches namely DivBaggingGP and DivNichGP. DivBaggingGP evolves the ensemble elements sequentially, while DivNichGP evolves them simultaneously. The experimental results showed that both DivBaggingGP and DivNichGP could obtain more interpretable routing policies than the single complex routing policy. DivNichGP can achieve better test performance than DivBaggingGP as well as the single routing policy evolved by the current state-of-the-art GPHH. This demonstrates the effectiveness of evolving both effective and interpretable routing policies using ensemble learning.