用大模型自动生成鲁棒网络设计启发式规则，减少人工与数据依赖。
AutoRNet: Automatically Optimizing Heuristics for Robust Network Design via Large Language Models
- 结合大模型与进化算法自动生成网络优化策略
- 在稀疏和密集尺度自由网络上性能超越现有方法
- 适合需要自动化网络设计的科研与工程人员
由于其NP-hard特性和复杂解空间，实现鲁棒网络设计极具挑战。当前方法从手工特征提取到深度学习虽有进展，但仍僵化，需人工设计且依赖大规模标注数据。为此，我们提出AutoRNet框架，将大语言模型（LLMs）与进化算法结合，生成鲁棒网络设计的启发式规则。通过设计领域特定提示词，利用领域知识生成先进启发式策略，并引入自适应适应度函数，在保持度分布的同时平衡收敛性与多样性。AutoRNet在稀疏与密集尺度自由网络上评估，显著降低对人工设计与大规模数据集的依赖，性能优于现有方法。
原文摘要 · Abstract (English)
Achieving robust networks is a challenging problem due to its NP-hard nature and complex solution space. Current methods, from handcrafted feature extraction to deep learning, have made progress but remain rigid, requiring manual design and large labeled datasets. To address these issues, we propose AutoRNet, a framework that integrates large language models (LLMs) with evolutionary algorithms to generate heuristics for robust network design. We design network optimization strategies to provide domain-specific prompts for LLMs, utilizing domain knowledge to generate advanced heuristics. Additionally, we introduce an adaptive fitness function to balance convergence and diversity while maintaining degree distributions. AutoRNet is evaluated on sparse and dense scale-free networks, outperforming current methods by reducing the need for manual design and large datasets.
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