Abstract
Training deep learning models for solving the Travelling Salesman Problem (TSP) directly on large instances is computationally challenging. An approach to tackle large-scale TSPs is through identifying elements in the model or training procedure that promotes out-of-distribution (OoD) generalization, i.e., generalization to samples larger than those seen in training. The state-of-the-art TSP solvers based on Graph Neural Networks (GNNs) follow different strategies to represent the TSP instances as input graphs. In this paper, we conduct experiments comparing different graph representations finding features that lead to a better OoD generalization.
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Acknowledgement
R. Menchaca and E. Zamora would like to acknowledge the support provided by CIC-IPN in carrying out this research. This work was economically supported by SIP-IPN (grant numbers 20211096, 20210316). O. Gutiérrez acknowledges CONACYT for the scholarship granted towards pursuing his postgraduate studies.
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Gutiérrez, O., Zamora, E., Menchaca, R. (2021). Graph Representation for Learning the Traveling Salesman Problem. In: Roman-Rangel, E., Kuri-Morales, Á.F., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2021. Lecture Notes in Computer Science(), vol 12725. Springer, Cham. https://doi.org/10.1007/978-3-030-77004-4_15
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