超越节点：用于自动启发式设计中高效MCTS的族级选择
Beyond the Node: Clade-level Selection for Efficient MCTS in Automatic Heuristic Design
AI总结：
Clade-AHD通过族级贝叶斯信念替代节点级估计，有效解决自动启发式设计中MCTS的过度探索问题，提升决策可靠性并降低计算成本。
摘要：
尽管蒙特卡洛树搜索（MCTS）在基于大语言模型（LLM）的自动启发式设计（AHD）中展现出潜力，但其在有限的启发式评估计算预算下容易出现过度探索的倾向。为了解决这一限制，我们提出了一种高效的框架，即Clade-AHD，该框架用族级贝叶斯信念取代节点级点估计。通过将后代评估聚合到贝塔分布中，并在这些信念上执行汤普森采样，Clade-AHD明确建模不确定性以指导探索，从而在稀疏和嘈杂的评估下实现更可靠的决策。在复杂的组合优化问题上的广泛实验表明，Clade-AHD在显著降低计算成本的同时，始终优于最先进的方法。源代码可在：https://github.com/Mriya0306/Clade-AHD 上公开获取。
英文摘要：
While Monte Carlo Tree Search (MCTS) shows promise in Large Language Model (LLM) based Automatic Heuristic Design (AHD), it suffers from a critical over-exploitation tendency under the limited computational budgets required for heuristic evaluation. To address this limitation, we propose Clade-AHD, an efficient framework that replaces node-level point estimates with clade-level Bayesian beliefs. By aggregating descendant evaluations into Beta distributions and performing Thompson Sampling over these beliefs, Clade-AHD explicitly models uncertainty to guide exploration, enabling more reliable decision-making under sparse and noisy evaluations. Extensive experiments on complex combinatorial optimization problems demonstrate that Clade-AHD consistently outperforms state-of-the-art methods while significantly reducing computational cost. The source code is publicly available at: https://github.com/Mriya0306/Clade-AHD.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
