A2DEPT: Large Language Model-Driven Automated Algorithm Design via Evolutionary Program Trees
Designing heuristics for combinatorial optimization problems (COPs) is a fundamental yet challenging task that traditionally requires extensive domain expertise. Recently, Large Language Model (LLM)-based Automated Heuristic Design (AHD) has shown promise in autonomously generating heuristic components with minimal human intervention. However, most existing LLM-based AHD methods enforce fixed algorithmic templates to ensure executability, which confines the search to component-level tuning and limits system-level algorithmic expressiveness. To enable open-ended solver synthesis beyond rigid templates, we propose Automated Algorithm Design via Evolutionary Program Trees (A2DEPT), which tre...
| Authors | Bin Chen, Shouliang Zhu, Beidan Liu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 27 April 2026 |
| Last updated at source | 27 April 2026 |
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