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

TL;DR

Clade-AHD enhances MCTS-based heuristic design by using clade-level Bayesian beliefs and Thompson Sampling, leading to better exploration, improved performance, and reduced computational costs in complex optimization tasks.

Contribution

This paper introduces Clade-AHD, a novel framework that replaces node-level estimates with clade-level Bayesian beliefs for more efficient MCTS in heuristic design.

Findings

Outperforms state-of-the-art methods in complex optimization tasks.

Reduces computational cost significantly.

Provides more reliable decision-making under sparse evaluations.

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.