Beyond the Node: Clade-level Selection for Efficient MCTS in Automatic Heuristic Design

2026-01-31

AI Summary

This work addresses the over-exploitation issue in Monte Carlo Tree Search (MCTS) for automated heuristic design under limited computational budgets. To mitigate this, the authors propose Clade-AHD, a novel framework that introduces, for the first time, a clade-level Bayesian belief modeling mechanism. Instead of relying on traditional node-level point estimates, Clade-AHD aggregates subtree evaluations using Beta distributions and employs Thompson sampling to guide exploration decisions. This approach effectively balances exploration and exploitation in sparse and noisy evaluation environments, substantially improving heuristic quality. Experimental results demonstrate that Clade-AHD outperforms existing methods on complex combinatorial optimization tasks while significantly reducing computational overhead.

Technology Category

• Search and Optimization: Heuristic Search
• Reasoning under Uncertainty: Stochastic Optimization
• Constraint Satisfaction and Optimization: Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

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.

Problem

• Monte Carlo Tree Search
• Automatic Heuristic Design
• over-exploitation
• computational budget
• Large Language Model

Innovation

• Clade-level Bayesian belief
• Thompson Sampling
• Monte Carlo Tree Search
• Automatic Heuristic Design
• Uncertainty-aware exploration