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<p>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 [<CitationRef CitationID="CR1">1</CitationRef>] presented at EvoCOP2022.</p>

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Combining Monte Carlo Tree Search and Heuristic Search for Weighted Vertex Coloring

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月13日

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  • Cyril Grelier,
  • Olivier Goudet,
  • Jin-Kao Hao

摘要

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 [1] presented at EvoCOP2022.