Combining Monte Carlo Tree Search and Heuristic Search for Weighted Vertex Coloring
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Abstract (arXiv)
This work investigates the Monte Carlo Tree Search (MCTS) method combined with dedicated heuristics for solving the Weighted Vertex Coloring Problem. In addition to the basic MCTS algorithm, we study several MCTS variants where the conventional random simulation is replaced by other simulation strategies including greedy and local search heuristics. We conduct experiments on well-known benchmark instances to assess these combined MCTS variants. We provide empirical evidence to shed light on the advantages and limits of each simulation strategy. This is an extension of the work of Grelier and al. presented at EvoCOP2022.
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References · 4
- 2016Reinforcement learning based local search for grouping problems: A case study on graph coloringcited by 6
- 2020Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problemscited by 4
- 2021A deep learning guided memetic framework for graph coloring problemscited by 3
- 2022On Monte Carlo Tree Search for Weighted Vertex Coloringcited by 1
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