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Neural approaches for combinatorial optimization (CO) equip a learning mechanism to discover powerful heuristics for solving complex real-world problems.
Bandit based monte-carlo planning
Levente Kocsis and Csaba Szepesvári · 2006
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The traveling salesman problem: a computational study
David L Applegate, Robert E Bixby, Vasek Chvatal, and William J Cook · 2006
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Efficient selectivity and backup operators in monte-carlo tree search
Rémi Coulom · 2007
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Monte carlo beam search
Tristan Cazenave · 2012
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Beam monte-carlo tree search
Hendrik Baier and Mark HM Winands · 2012
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A hybrid genetic algorithm for multidepot and periodic vehicle routing problems
Thibaut Vidal, Teodor Gabriel Crainic, Michel Gendreau, Nadia Lahrichi, and Walter Rei · 2012
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Algorithm and knowledge engineering for the tsptw problem
Stefan Edelkamp, Max Gath, Tristan Cazenave, and Fabien Teytaud · 2013
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Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly · 2015
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis · 2016
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Learning to branch in mixed integer programming
Elias Khalil, Pierre Le Bodic, Le Song, George Nemhauser, and Bistra Dilkina · 2016
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Neural combinatorial optimization with reinforcement learning
Irwan Bello, Hieu Pham, Quoc V. Le, Mohammad Norouzi, and Samy Bengio · 2017
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Learning combinatorial optimization algorithms over graphs
Elias Khalil, Hanjun Dai, Yuyu Zhang, Bistra Dilkina, and Le Song · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger · 2017
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An Extension of the Lin-Kernighan-Helsgaun TSP Solver for Constrained Traveling Salesman and Vehicle Routing Problems: Technical report
Keld Helsgaun · 2017
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Reinforcement learning for solving the vehicle routing problem
Mohammadreza Nazari, Afshin Oroojlooy, Lawrence Snyder, and Martin Takac · 2018
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Learning heuristics for the TSP by policy gradient
Michel Deudon, Pierre Cournut, Alexandre Lacoste, Yossiri Adulyasak, and Louis-Martin Rousseau · 2018
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Combinatorial optimization with graph convolutional networks and guided tree search
Zhuwen Li, Qifeng Chen, and Vladlen Koltun · 2018
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Learning beam search policies via imitation learning
Renato Negrinho, Matthew Gormley, and Geoffrey J Gordon · 2018
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Attention, learn to solve routing problems!
Wouter Kool, Herke van Hoof, and Max Welling · 2019
Cited alongside, same era.
Learning to perform local rewriting for combinatorial optimization
Xinyun Chen and Yuandong Tian · 2019
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Smart manufacturing scheduling with edge computing using multiclass deep q network
Chun-Cheng Lin, Der-Jiunn Deng, Yen-Ling Chih, and Hsin-Ting Chiu · 2019
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Learning local search heuristics for boolean satisfiability
Emre Yolcu and Barnabás Póczos · 2019
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An efficient graph convolutional network technique for the travelling salesman problem
Chaitanya K Joshi, Thomas Laurent, and Xavier Bresson · 2019
Cited alongside, same era.
Solving np-hard problems on graphs with extended alphago zero
V20.1: User’s Manual for CPLEX
IBM ILOG CPLEX Optimization Studio · 2020
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Deep policy dynamic programming for vehicle routing problems
Wouter Kool, Herke van Hoof, Joaquim Gromicho, and Max Welling · 2021
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Matrix encoding networks for neural combinatorial optimization
Yeong-Dae Kwon, Jinho Choo, Iljoo Yoon, Minah Park, Duwon Park, and Youngjune Gwon · 2021
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Learning improvement heuristics for solving routing problems
Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang, and Andrew Lim · 2021
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Learning to iteratively solve routing problems with dual-aspect collaborative transformer
Yining Ma, Jingwen Li, Zhiguang Cao, Wen Song, Le Zhang, Zhenghua Chen, and Jing Tang · 2021
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Kenshin Abe, Zijian Xu, Issei Sato, and Masashi Sugiyama · 2019
Cited alongside, same era.
Machine learning for combinatorial optimization: a methodological tour d’ horizon
Yoshua Bengio, Andrea Lodi, and Antoine Prouvost · 2020
Cited alongside, same era.
Pomo: Policy optimization with multiple optima for reinforcement learning
Yeong-Dae Kwon, Jinho Choo, Byoungjip Kim, Iljoo Yoon, Youngjune Gwon, and Seungjai Min · 2020
Cited alongside, same era.
Neural large neighborhood search for the capacitated vehicle routing problem
André Hottung and Kevin Tierney · 2020
Cited alongside, same era.
Learning 2-opt heuristics for the traveling salesman problem via deep reinforcement learning
Paulo R. de O. da Costa, Jason Rhuggenaath, Yingqian Zhang, and Alp Akcay · 2020
Cited alongside, same era.
Reinforcement learning with combinatorial actions: An application to vehicle routing
Arthur Delarue, Ross Anderson, and Christian Tjandraatmadja · 2020
Cited alongside, same era.
Learning to dispatch for job shop scheduling via deep reinforcement learning
Cong Zhang, Wen Song, Zhiguang Cao, Jie Zhang, Puay Siew Tan, and Xu Chi · 2020
Cited alongside, same era.
Minsu Kim, Jinkyoo Park, and Joungho Kim · 2021
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Learning to delegate for large-scale vehicle routing
Sirui Li, Zhongxia Yan, and Cathy Wu · 2021
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Neurolkh: Combining deep learning model with lin-kernighan-helsgaun heuristic for solving the traveling salesman problem
Liang Xin, Wen Song, Zhiguang Cao, and Jie Zhang · 2021
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Multi-decoder attention model with embedding glimpse for solving vehicle routing problems
Liang Xin, Wen Song, Zhiguang Cao, and Jie Zhang · 2021
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On joint learning for solving placement and routing in chip design
Ruoyu Cheng and Junchi Yan · 2021
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A graph placement methodology for fast chip design
Azalia Mirhoseini, Anna Goldie, Mustafa Yazgan, Joe Wenjie Jiang, Ebrahim Songhori, Shen Wang, Young-Joon Lee, Eric Johnson, Omkar Pathak, Azade Nazi, et al · 2021
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Generalize a small pre-trained model to arbitrarily large tsp instances
Zhang-Hua Fu, Kai-Bin Qiu, and Hongyuan Zha · 2021
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Learning beam search: Utilizing machine learning to guide beam search for solving combinatorial optimization problems
Marc Huber and Günther R Raidl · 2021
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Efficient active search for combinatorial optimization problems
André Hottung, Yeong-Dae Kwon, and Kevin Tierney · 2022
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Learning the travelling salesperson problem requires rethinking generalization
Chaitanya K Joshi, Quentin Cappart, Louis-Martin Rousseau, and Thomas Laurent · 2022
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Efficient neural neighborhood search for pickup and delivery problems
Yining Ma, Jingwen Li, Zhiguang Cao, Wen Song, Hongliang Guo, Yuejiao Gong, and Yeow Meng Chee · 2022
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Hybrid genetic search for the cvrp: Open-source implementation and swap* neighborhood
Thibaut Vidal · 2022
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