Fetching the paper…
Reading the bibliography…
Recent works using deep learning to solve the Traveling Salesman Problem (TSP) have focused on learning construction heuristics.
An effective heuristic algorithm for the traveling-salesman problem
Shen Lin and Brian W Kernighan · 1973
Earlier work this paper cites.
The euclidean travelling salesman problem is np-complete
Christos H Papadimitriou · 1977
Earlier work this paper cites.
Neural computation of decisions in optimization problems
John J Hopfield and David W Tank · 1985
Earlier work this paper cites.
Self-organizing feature maps and the travelling salesman problem
Bernard Angeniol, Gael De La Croix Vaubois, and Jean-Yves Le Texier · 1988
Earlier work this paper cites.
Tsplib—a traveling salesman problem library
Gerhard Reinelt · 1991
Earlier work this paper cites.
Learning improvement heuristics for solving the travelling salesman problem
Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang, and Andrew Lim · 1991
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Polynomial time approximation schemes for euclidean traveling salesman and other geometric problems
Sanjeev Arora · 1998
Earlier work this paper cites.
The traveling salesman problem: a computational study
David L Applegate, Robert E Bixby, Vasek Chvatal, and William J Cook · 2006
Cited alongside, same era.
First vs. best improvement: An empirical study
Pierre Hansen and Nenad Mladenović · 2006
Cited alongside, same era.
General k-opt submoves for the lin–kernighan tsp heuristic
Keld Helsgaun · 2009
Cited alongside, same era.
Comparison of neural networks for solving the travelling salesman problem
Bert FJ La Maire and Valeri M Mladenov · 2012
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
Cited alongside, same era.
Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly · 2015
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
Later among the works it cites.
Machine learning for combinatorial optimization: a methodological tour d’horizon
Yoshua Bengio, Andrea Lodi, and Antoine Prouvost · 2018
Later among the works it cites.
Learning heuristics for the tsp by policy gradient
Michel Deudon, Pierre Cournut, Alexandre Lacoste, Yossiri Adulyasak, and Louis-Martin Rousseau · 2018
Later among the works it cites.
Xianyan Jia, Shutao Song, Wei He, Yangzihao Wang, Haidong Rong, Feihu Zhou, Liqiang Xie, Zhenyu Guo, Yuanzhou Yang, Liwei Yu, et al · 2018
Later among the works it cites.
Boosting combinatorial problem modeling with machine learning
Michele Lombardi and Michela Milano · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
Cited alongside, same era.
Neural combinatorial optimization with reinforcement learning
Irwan Bello and Hieu Pham · 2017
Cited alongside, same era.
Learning combinatorial optimization algorithms over graphs
Elias Khalil, Hanjun Dai, Yuyu Zhang, Bistra Dilkina, and Le Song · 2017
Cited alongside, same era.
Or-tools
Laurent Perron and Vincent Furnon
Cited in the paper.
Graph Attention Networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
Later among the works it cites.
An efficient graph convolutional network technique for the travelling salesman problem
Chaitanya K Joshi, Thomas Laurent, and Xavier Bresson · 2019
Later among the works it cites.
Attention, learn to solve routing problems!
Wouter Kool, Herke van Hoof, and Max Welling · 2019
Later among the works it cites.