[1] Wu, Q., Hao, J.-K.: A review on algorithms for maximum clique problems. Eur. J. Oper. Res. 242(3), 693-709 (2015)
[2] Leese, R., Hurley, S.: Methods and Algorithms for Radio Channel Assignment. Oxford University Press, Oxford (2002)
[3] Montemanni, R., Smith, D.H., Allen, S.M.: Lower bounds for fixed spectrum frequency assignment. Ann. Oper. Res. 107, 237-250 (2001)
[4] Smith, D.H., Montemanni, R.: A new table of permutation codes. Des. Codes Crypt. 63(2), 241-253 (2012)
[5] Smith, D.H., Montemanni, R.: Permutation codes with specified packing radius. Des. Codes Crypt. 69(1), 95-106 (2013)
[6] Li, Z., Chen, Q., Koltun, V.: Combinatorial optimization with graph convolutional networks and guided tree search. In: Proceedings of the 32nd Conference on Neural Information Processing Systems, pp. 537-546 (2018)
[7] Carraghan, R., Pardalos, P.M.: An exact algorithm for the maximum clique problem. Oper. Res. Lett. 9(6), 375-382 (1990)
[8] Montemanni, R., Barta, J., Smith, D.H.: Graph colouring and branch and bound approaches for permutation code algorithms. In: Rocha, A., et al. (eds.) New Advances in Information Systems and Technologies. Advances in Intelligent Systems and Computing, vol. 444, pp. 223-232. Springer, Cham (2016)
[9] Östergård, P.R.J.: A fast algorithm for the maximum clique problem. Discrete Appl. Math. 120, 197-207 (2002)
[10] Jin, Y., Hao, J.-K.: General swap-based multiple neighborhood tabu search for the maximum independent set problem. Eng. Appl. Artif. Intel. 37, 20-33 (2015)
[11] Smith, D.H., Perkins, S., Montemanni, R.: Solving the maximum clique problem with a hybrid algorithm. Int. J. Metaheuristics 7(2), 152-175 (2019)
[12] Smith, D.H., Montemanni, R., Perkins, S.: The use of an exact algorithm within a tabu search Maximum Clique algorithm. Algorithms 13(10), 253 (2020)
[13] He, H., Daumé H. III, Eisner, J.: Learning to search in branch and bound algorithms. In: Proceedings of the 27th Conference on Advances in Neural Information Processing Systems, pp. 3293-3301 (2014)
[14] Vinyals, O., Fortunato, M., Jaitly, N.: Pointer networks. In: Proceedings of 28th Conference on Neural Information Processing Systems, pp. 2692-2700 (2015)
[15] Mele, U.J., Chou, X., Gambardella, L.M., Montemanni, R: Reinforcement learning and additional rewards for the traveling salesman problem. In: Proceedings of the 7th IEEE International Conference on Industrial Engineering and Applications, pp. 170-176 (2020)
[16] Bello, I., Pham, H., Le, Q.V., Norouzi, M., Bengio, S.: Neural combinatorial optimization with reinforcement learning (2016). arXiv:1611.09940
[17] Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Proceedings of the 30th Conference on Neural Information Processing Systems, pp. 3844-3852 (2016)
[18] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th International Conference on Learning Representations (2017)
[19] Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., van den Driessche, G., et al.: Mastering the game of Go with deep neural networks and tree search. Nature 529, 484-489 (2016)
[20] Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., et al.: Mastering the game of Go without human knowledge. Nature 550, 354-359 (2017)
[21] Schrittwieser, J., Antonoglou, I., Hubert, T., Simonyan, K., Sifre, L., Schmitt, S., et al.: Mastering Atari, Go, chess and shogi by planning with a learned model. Nature 588, 604-609 (2020)
[22] Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on International Conference on Machine Learning. Omnipress (2010)
[23] Guzmán-Rivera, A., Batra, D., Kohli, P.: Multiple choice learning: learning to produce multiple structured outputs. In: Proceedings of the 25th Conference on Neural Information Processing Systems, pp. 1799-1807 (2012)
[24] Andrade, D.V., Resende, M.G.C., Werneck, R.F.: Fast local search for the maximum independent set problem. J. Heuristics 18(4), 525-547 (2012)
[25] Battiti, R., Protasi, M.: Reactive local search for the maximum clique problem. Algorithmica 29(4), 610-637 (2001)
[26] Feo, T.A., Resende, M.G.C., Smith, S.H.: A greedy randomized adaptive search procedure for maximum independent set. Oper. Res. 42(5), 860-878 (1994)
[27] Akiba, T., Iwata, Y.: Branch-and-reduce exponential/FPT algorithms in practice: a case study of vertex cover. Theor. Comput. Sci. 609(I), 211-225 (2016)
[28] McKay, B.D.: Practical graph isomorphism. Congr. Numer. 30, 45-87 (1980)
[29] McKay, B.D., Piperno, A.: Practical graph isomorphism, II. J. Symb. Comput. 60, 94-112 (2014)
[30] Junttila, T., Kaski, P.: Conflict propagation and component recursion for canonical labeling. In: Marchetti-Spaccamela A., Segal M. (eds.) Theory and Practice of Algorithms in (Computer) Systems, pp. 151-162 (2011)
[31] Montemanni, R., Barta, J., Smith, D.H.: Permutation codes: a branch and bound approach. In: Proceedings of the International Conference on Pure Mathematics, Applied Mathematics, Computational Methods, pp. 86-90 (2014)
[32] Xu, K., Boussemart, F., Hemery, F., Lecoutre, C.: Random constraint satisfaction: easy generation of hard (satisfiable) instances. Artif. Intel. 171, 514-534 (2007)
[33] Xu, K., Boussemart, F., Hemery, F., Lecoutre, C.: A simple model to generate hard satisfiable instances. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence, pp. 337-342 (2005)
[34] Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of 3rd International Conference on Learning Representations, San Diego. http://arxiv.org/abs/1412.6980 (2015)
