Automatically optimizing heuristics for robust scale-free network design via large language models.

ABSTRACT: Achieving scale-free robust networks is a challenging problem due to its NP-hard nature and the vast, complex, high-dimensional solution space. Current methods, from handcrafted feature extraction to deep learning approaches, have made certain advancements but remain rigid and complex, often requiring manual design, trial and error, and large amounts of labeled data. To address these challenges, we propose AutoRNet, a novel framework that integrates large language models (LLMs) with evolutionary algorithms to generate complete heuristics for robust network design, supported by expert-crafted Network Optimization Strategies (NOSs). With the intrinsic properties of robust scale-free network structures in mind, NOS-based variation operations provide domain-specific prompts for LLMs, leveragin

SUBMITTER: Yu H 

PROVIDER: S-EPMC12635234 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature