A2DEPT: Large Language Model-Driven Automated Algorithm Design via Evolutionary Program Trees

Computer Science > Artificial Intelligence arXiv:2604.24043 (cs) [Submitted on 27 Apr 2026] Title:A2DEPT: Large Language Model-Driven Automated Algorithm Design via Evolutionary Program Trees Authors:Bin Chen, Shouliang Zhu, Beidan Liu, Yong Zhao, Tianle Pu, Huichun Li, Zhengqiu Zhu View a PDF of the paper titled A2DEPT: Large Language Model-Driven Automated Algorithm Design via Evolutionary Program Trees, by Bin Chen and 6 other authors View PDF HTML (experimental) Abstract: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 treats LLMs as system-level algorithm architects. A2DEPT explores the vast program space via a tree-structured evolutionary search with hybrid selection and hierarchical operators, enabling iterative refinement of complete algorithms. To make open-ended generation practical, we enforce executability with a lightweight program-maintenance loop that performs feedback-driven repair. In experiments, A2DEPT consistently outperforms representative LLM-based baselines on both standard and highly constrained benchmarks. On the standard benchmarks, it reduces the mean normalized optimality gap by 9.8% relative to the strongest competing AHD baseline. Comments: Preprint Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2604.24043 [cs.AI] (or arXiv:2604.24043v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2604.24043 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Shouliang Zhu [view email] [v1] Mon, 27 Apr 2026 05:07:10 UTC (1,477 KB) Full-text links: Access Paper: View a PDF of the paper titled A2DEPT: Large Language Model-Driven Automated Algorithm Design via Evolutionary Program Trees, by Bin Chen and 6 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle…

This excerpt is published under fair use for community discussion. Read the full article at arXiv.org.