Graph Representation for Learning the Traveling Salesman Problem

Omar Gutiérrez, Erik Zamora, Ricardo Menchaca

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaPattern Recognition - 13th Mexican Conference, MCPR 2021, Proceedings
EditoresEdgar Roman-Rangel, Ángel Fernando Kuri-Morales, José Francisco Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa, José Arturo Olvera-López
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas153-162
Número de páginas10
ISBN (versión impresa)9783030770037
DOI
EstadoPublicada - 2021
Evento13th Mexican Conference on Pattern Recognition, MCPR 2021 - Virtual, Online
Duración: 23 jun. 202126 jun. 2021

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen12725 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia13th Mexican Conference on Pattern Recognition, MCPR 2021
CiudadVirtual, Online
Período23/06/2126/06/21

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