Solving NP-hard/complete combinatorial problems with neural networks is a
challenging research area that aims to surpass classical approximate
algorithms. The long-term objective is to outperform hand-designed heuristics
for NP-hard/complete problems by learning to generate superior solutions solely
from training data. The Travelling Salesman Problem (TSP) is a prominent
combinatorial optimisation problem often targeted by such approaches. However,
current neural-based methods for solving TSP often overlook the inherent
"algorithmic" nature of the problem. In contrast, heuristics designed for TSP
frequently leverage well-established algorithms, such as those for finding the
minimum spanning tree. In this paper, we propose leveraging recent advancements
in neural algorithmic reasoning to improve the learning of TSP problems.
Specifically, we suggest pre-training our neural model on relevant algorithms
before training it on TSP instances. Our results demonstrate that, using this
learning setup, we achieve superior performance compared to non-algorithmically
informed deep learning models