English(EN) DGA$_2$D: Directed Graph-Guided Automated Algorithm Design with Large Language Models

LLM驱动新型定向图框架实现自动化算法设计

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-01 14:59

研究人员开发了DGA$_2$D，一个利用大型语言模型（LLM）解决复杂NP-hard组合优化问题的新型自动化算法设计框架。与依赖固定模板的先前方法不同，DGA$_2$D将算法设计空间构建为定向图，从而能够更自主、更可靠地生成功能算子。与现有的基于LLM的技术相比，该方法在12个不同的优化问题上，平均归一化差距最多可减少10.96个百分点，显示出显著的改进。 AI

影响增强了LLM解决复杂优化问题的能力，有望加速需要此类解决方案的领域的研究和开发。

排序理由详细介绍使用LLM进行自动化算法设计新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.NE (Neural & Evolutionary) 阅读 →

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arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Liyuanjun Lai · 2026-08-01 14:59

DGA$_2$D: 大型语言模型引导的定向图自动算法设计

The rapid development of Large Language Models (LLMs) has opened new avenues for Automated Heuristic Design (AHD) for solving NP-hard combinatorial optimization problems (COPs). However, existing LLM-driven AHD methods are largely confined to rigid solver templates, relegating th…

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DGA$_2$D: 大型语言模型引导的定向图自动算法设计

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