arXiv cs.AI· Hanyong Xu, Zhaolai Dang, Tong Zhang·· 14 小时前AI 评分18
ReEvo 设计的模因算法：超越 LLM-GA 的安全流体天线系统
Beyond LLM-GA: Secure Fluid Antenna Systems with ReEvo-Designed Memetic Algorithm
AI 导读
研究提出基于反射进化（ReEvo）的模因算法，用 LLM 离线进化专用的交叉、变异与局部搜索算子，再嵌入模因搜索框架，执行时无需在线 LLM 查询。在相同迭代次数下，该算法的安全总速率高于传统 GA 和当前最优的 LLM-GA，面向军事、卫星与物联网场景的安全流体天线端口选择问题。
正文 · 原文
Abstract:Fluid antenna systems (FASs) offer significant spatial flexibility, yet securing them against eavesdropping is critical for practical FAS deployment in military, satellite, and internet-of-things networks. Although large language model (LLM)-assisted genetic algorithms (LLM-GAs) can address this secure FAS port selection problem, whether further algorithmic improvement is possible warrants deeper investigation. To this end, we propose a memetic algorithm based on reflective evolution (ReEvo). Unlike the state-of-the-art LLM-GAs, which design only crossover or mutation operators with an LLM, our algorithm leverages an LLM to evolve dedicated crossover, mutation, and local-search operators offline. These operators are then embedded into a memetic search framework, thereby obviating any online LLM queries during execution. Simulation results at equal generation counts demonstrate that our proposed algorithm achieves a higher secure sum-rate than the conventional GA and the state-of-the-art LLM-GAs.
| Comments: | Accepted by WCSP 2026 |
| Subjects: | Information Theory (cs.IT); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.10235 [cs.IT] |
| (or arXiv:2610.10235v2 [cs.IT] for this version) | |
| https://doi.org/10.48550/arXiv.2610.10235 arXiv-issued DOI via DataCite |
Submission history
From: Tong Zhang [view email]
[v1]
Wed, 7 Oct 2026 15:20:52 UTC (250 KB)
[v2]
Mon, 26 Oct 2026 13:18:46 UTC (250 KB)
来源：arXiv cs.AI · arxiv.org