Abstract
This work presents the first study of using the popular Monte Carlo Tree Search (MCTS) method combined with dedicated heuristics for solving the Weighted Vertex Coloring Problem. Starting with the basic MCTS algorithm, we gradually introduce a number of algorithmic variants where MCTS is extended by various simulation strategies including greedy and local search heuristics. We conduct experiments on well-known benchmark instances to assess the value of each studied combination. We also provide empirical evidence to shed light on the advantages and limits of each strategy.
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Acknowledgements
We would like to thank the reviewers for their useful comments. We thank Dr. Wen Sun [20], Dr. Yiyuan Wang, [22] and Pr. Bruno Nogueira [16] for sharing their codes. This work was granted access to the HPC resources of IDRIS (Grant No. 2020-A0090611887) from GENCI and the Centre Régional de Calcul Intensif des Pays de la Loire.
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Grelier, C., Goudet, O., Hao, JK. (2022). On Monte Carlo Tree Search for Weighted Vertex Coloring. In: Pérez Cáceres, L., Verel, S. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2022. Lecture Notes in Computer Science, vol 13222. Springer, Cham. https://doi.org/10.1007/978-3-031-04148-8_1
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