Steven James, George Konidaris, and Benjamin Rosman. An analysis of monte carlo tree search. AAAI Conference on Artificial Intelligence, 31(1), Feb. 2017. URL https://ods.aaai.org/index.php/AAAI/article/view/11028.
Nikolaos Karalias and Andreas Loukas. Erdos goes neural: an unsupervised learning framework for combinatorial optimization on graphs. Advances in neural information processing systems (NeurIPS), 33:6659-6672, 2020.
Maryam Karimi-Mamaghan, Mehrdad Mohammadi, Patrick Meyer, Amir Mohammad Karimi-Mamaghan, and El-Ghazali Talbi. Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art. *EJOR*, 296(2):393–422, 2022.
Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. International Conference on Learning Representations (ICLR), 2017.
Henrique Lemos, Marcelo Prates, Pedro Avelar, and Luis Lamb. Graph colouring meets deep learning: Effective graph neural network models for combinatorial problems. In International Conference on Tools for Artificial Intelligence (ICTAI), pages 879-885. IEEE, 2019.
Jure Leskovec and Andrej Krevl. SNAP Datasets: Stanford large network dataset collection. http://snap.stanford.edu/data, June 2014.
Kaiwen Li, Tao Zhang, Rui Wang, Yuheng Wang, Yi Han, and Ling Wang. Deep reinforcement learning for combinatorial optimization: Covering salesman problems. IEEE Transactions on Cybernetics, 52(12):13142-13155, 2021.
Zhuwen Li, Qifeng Chen, and Vladlen Koltun. Combinatorial optimization with graph convolutional networks and guided tree search. Advances in neural information processing systems (NeurIPS), 31, 2018.
Nina Mazyavkina, Sergey Sviridov, Sergei Ivanov, and Evgeny Burnaev. Reinforcement learning for combinatorial optimization: A survey. COR, 134:105400, 2021.
Christopher Morris, Nils M. Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann. Tudataset: A collection of benchmark datasets for learning with graphs. In International Conference on Machine Learning (ICML), 2020. URL www.graphlearning.io.
Mohammadreza Nazari, Afshin Oroojlooy, Lawrence Snyder, and Martin Takác. Reinforcement learning for solving the vehicle routing problem. Advances in neural information processing systems (NeurIPS), 31, 2018.
Alex W. Nowak, Soledad Villar, Afonso S. Bandeira, and Joan Bruna. A note on learning algorithms for quadratic assignment with graph neural networks. ArXiv, abs/1706.07450, 2017.
Junyoung Park, Sanjar Bakhtiyar, and Jinkyoo Park. Schedulenet: Learn to solve multi-agent scheduling problems with reinforcement learning, 2021.
Marcelo Prates, Pedro HC Avelar, Henrique Lemos, Luis C Lamb, and Moshe Y Vardi. Learning to solve np-complete problems: A graph neural network for decision tsp. In AAAI Conference on Artificial Intelligence, volume 33, pages 4731-4738, 2019.
Martin JA Schuetz, J Kyle Brubaker, and Helmut G Katzgraber. Combinatorial optimization with physics-inspired graph neural networks. Nature Machine Intelligence, 4(4):367-377, 2022a.
Martin JA Schuetz, J Kyle Brubaker, Zhihuai Zhu, and Helmut G Katzgraber. Graph coloring with physics-inspired graph neural networks. Physical Review Research, 4(4):043131, 2022b.
Ulrich Schwalbe and Paul Walker. Zermelo and the early history of game theory. Games and economic behavior, 34(1):123-137, 2001.
Mohamed A Wahby Shalaby, Ayman R Mohammed, and Sally S Kassem. Supervised fuzzy c-means techniques to solve the capacitated vehicle routing problem. IAJIT, 18(3A):452-463, 2021.