Abstract
In this paper, we consider adaptations of Monte Carlo Search methods on binary decision trees where actions are simulated using heuristics and where choices are made deterministically or stochastically. We explain how these adaptations are fitted for combinatorial problems such as element selection problems in order to compete with other approximate resolution methods such as metaheuristics. We present results on a theoretical problem (Set Covering) and on an applied problem (Pulse Repetition Frequency Selection) with different simulation heuristics. We then discuss the usefulness of these new methods based on the characteristics of the problems and on the quality of the simulation heuristics used to construct the decision tree.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Adamo, T., Ghiani, G., Guerriero, E., Pareo, D.: A surprisal-based greedy heuristic for the set covering problem. Algorithms 16(7) (2023). https://doi.org/10.3390/a16070321, https://www.mdpi.com/1999-4893/16/7/321
Ahn, S., Lee, H., Jung, B.: Medium PRF set selection for pulsed doppler radars using simulated annealing. In: 2011 IEEE RadarCon (RADAR), pp. 090–094 (2011). https://doi.org/10.1109/RADAR.2011.5960505
Beasley, J.: An algorithm for set covering problem. Eur. J. Oper. Res. 31(1), 85–93 (1987). https://doi.org/10.1016/0377-2217(87)90141-X, https://www.sciencedirect.com/science/article/pii/037722178790141X
Brunner, J.: A recursive method for calculating binary integration detection probabilities. IEEE Trans. Aerosp. Electron. Syst. 26(6), 1034–1035 (1990). https://doi.org/10.1109/7.62256
Cazenave, T.: Nested Monte-Carlo search. In: IJCAI International Joint Conference on Artificial Intelligence, pp. 456–461 (2009)
Cazenave, T.: Generalized nested rollout policy adaptation. In: Monte Search at IJCAI (2020)
Cazenave, T.: Generalized nested rollout policy adaptation with limited repetitions. arXiv preprint arXiv:2401.10420 (2024)
Cazenave, T., Teytaud, F.: Application of the nested rollout policy adaptation algorithm to the traveling salesman problem with time windows. In: Learning and Intelligent Optimization - 6th International Conference, LION 6, pp. 42–54 (2012)
Chvatal, V.: A greedy heuristic for the set-covering problem. Math. Oper. Res. 4(3), 233–235 (1979). http://www.jstor.org/stable/3689577
Gelly, S., Silver, D.: Monte-Carlo tree search and rapid action value estimation in computer go. Artif. Intell. 175(11), 1856–1875 (2011). https://doi.org/10.1016/j.artint.2011.03.007
Harvey, W.D., Ginsberg, M.L.: Limited discrepancy search. In: IJCAI, pp. 607–615. Morgan Kaufmann (1995)
Hughes, E., Alabaster, C.: Medium prf radar prf optimisation using evolutionary algorithms. In: Proceedings of the 2003 IEEE Radar Conference (Cat. No. 03CH37474), pp. 192–197 (2003). https://doi.org/10.1109/NRC.2003.1203401
Karp, R.M.: Reducibility among Combinatorial Problems, pp. 85–103. Springer, Boston (1972). https://doi.org/10.1007/978-1-4684-2001-2_9
Rosin, C.D.: Nested rollout policy adaptation for Monte Carlo tree search. In: IJCAI, pp. 649–654 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ardon, M., Briheche, Y., Cazenave, T. (2025). Binarized Monte Carlo Search for Selection Problems. In: Festa, P., Ferone, D., Pastore, T., Pisacane, O. (eds) Learning and Intelligent Optimization. LION 2024. Lecture Notes in Computer Science, vol 14990. Springer, Cham. https://doi.org/10.1007/978-3-031-75623-8_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-75623-8_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-75622-1
Online ISBN: 978-3-031-75623-8
eBook Packages: Computer ScienceComputer Science (R0)