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We present the Generative Flow Ant Colony Sampler (GFACS), a novel meta-heuristic method that hierarchically combines amortized inference and parallel stochastic search.
A method for solving traveling salesman problems
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Integer programming formulation of traveling salesman problems
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Steps toward artificial intelligence
Minsky, M. (1961) · 1961
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Optimization by simulated annealing
Kirkpatrick, S., Gelatt Jr, C. D., and Vecchi, M. P. (1983) · 1983
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TSPLIB—A traveling salesman problem library
Reinelt, G. (1991) · 1991
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A genetic algorithm for bin packing and line balancing
Falkenauer, E. and Delchambre, A. (1992) · 1992
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, R. J. (1992) · 1992
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Learning bayesian networks: Search methods and experimental results
Chickering, D. M., Geiger, D., and Heckerman, D. (1995) · 1995
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Ant system: optimization by a colony of cooperating agents
Dorigo, M., Maniezzo, V., and Colorni, A. (1996) · 1996
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Ant colony system: a cooperative learning approach to the traveling salesman problem
Dorigo, M. and Gambardella, L. M. (1997) · 1997
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Solving the orienteering problem through branch-and-cut
Fischetti, M., Gonzalez, J. J. S., and Toth, P. (1998) · 1998
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MAX–MIN ant system
Stützle, T. and Hoos, H. H. (2000) · 2000
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Large-sample learning of bayesian networks is np-hard
Chickering, M., Heckerman, D., and Meek, C. (2004) · 2004
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Ant colony optimization theory: A survey
Dorigo, M. and Blum, C. (2005) · 2005
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Concorde TSP solver
Applegate, D., Bixby, R., Chvatal, V., and Cook, W. (2006) · 2006
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Ant colony optimization
Dorigo, M., Birattari, M., and Stutzle, T. (2006) · 2006
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Playing atari with deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M. (2013) · 2013
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C. (2015) · 2015
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Deep learning
LeCun, Y., Bengio, Y., and Hinton, G. (2015) · 2015
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Pointer networks
Vinyals, O., Fortunato, M., and Jaitly, N. (2015) · 2015
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Neural combinatorial optimization with reinforcement learning
Bello, I., Pham, H., Le, Q. V., Norouzi, M., and Bengio, S. (2016) · 2016
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An extension of the Lin-Kernighan-Helsgaun TSP solver for constrained traveling salesman and vehicle routing problems
Helsgaun, K. (2017) · 2017
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Learning combinatorial optimization algorithms over graphs
Khalil, E., Dai, H., Zhang, Y., Dilkina, B., and Song, L. (2017) · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M. (2017) · 2017
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New benchmark instances for the capacitated vehicle routing problem
Uchoa, E., Pecin, D., Pessoa, A., Poggi, M., Vidal, T., and Subramanian, A. (2017) · 2017
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Sigmoid-weighted linear units for neural network function approximation in reinforcement learning
Elfwing, S., Uchibe, E., and Doya, K. (2018) · 2018
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Reinforcement learning for solving the vehicle routing problem
Nazari, M., Oroojlooy, A., Snyder, L., and Takac, M. (2018) · 2018
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Graph attention networks
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y. (2018) · 2018
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Attention, learn to solve routing problems!
Kool, W., van Hoof, H., and Welling, M. (2019) · 2019
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Large neighborhood search
Pisinger, D. and Ropke, S. (2019) · 2019
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Review of job shop scheduling research and its new perspectives under industry 4.0
Zhang, J., Ding, G., Zou, Y., Qin, S., and Fu, J. (2019) · 2019
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Learning what to defer for maximum independent sets
Ahn, S., Seo, Y., and Shin, J. (2020) · 2020
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Gflownet foundations
Bengio, Y., Lahlou, S., Deleu, T., Hu, E. J., Tiwari, M., and Bengio, E. (2023) · 2023
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RL4CO: a unified reinforcement learning for combinatorial optimization library
Berto, F., Hua, C., Park, J., Kim, M., Kim, H., Son, J., Kim, H., Kim, J., and Park, J. (2023) · 2023
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Combinatorial optimization with policy adaptation using latent space search
Chalumeau, F., Surana, S., Bonnet, C., Grinsztajn, N., Pretorius, A., Laterre, A., and Barrett, T. (2023) · 2023
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Select and optimize: Learning to aolve large-scale tsp instances
Cheng, H., Zheng, H., Cong, Y., Jiang, W., and Pu, S. (2023) · 2023
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BQ-NCO: Bisimulation quotienting for efficient neural combinatorial optimization
Drakulic, D., Michel, S., Mai, F., Sors, A., and Andreoli, J.-M. (2023) · 2023
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d O Costa, P. R., Rhuggenaath, J., Zhang, Y., and Akcay, A. (2020) · 2020
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Neural large neighborhood search for the capacitated vehicle routing problem
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Kwon, Y.-D., Choo, J., Kim, B., Yoon, I., Gwon, Y., and Min, S. (2020) · 2020
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Naseri, G. and Koffas, M. A. (2020) · 2020
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Generalize learned heuristics to solve large-scale vehicle routing problems in real-time
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Pointerformer: Deep reinforced multi-pointer transformer for the traveling salesman problem
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Learning to CROSS exchange to solve min-max vehicle routing problems
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Learning to search feasible and infeasible regions of routing problems with flexible neural k-opt
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GFlowNets and variational inference
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Better training of GFlowNets with local credit and incomplete trajectories
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Meta-SAGE: scale meta-learning scheduled adaptation with guided exploration for mitigating scale shift on combinatorial optimization
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Revisiting sampling for combinatorial optimization
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DIFUSCO: Graph-based diffusion solvers for combinatorial optimization
Sun, Z. and Yang, Y. (2023) · 2023
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DeepACO: Neural-enhanced ant systems for combinatorial optimization
Ye, H., Wang, J., Cao, Z., Liang, H., and Li, Y. (2023) · 2023
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Let the flows tell: Solving graph combinatorial problems with gflownets
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