# Beyond LLM-GA: Secure Fluid Antenna Systems with ReEvo-Designed Memetic Algorithm

arXiv: https://arxiv.org/abs/2610.10235 · HTML: https://mlsanity.com/paper/2610.10235/

## Key facts

- No citing papers in the map yet (data through 7 Oct 2026).
- Builds on 1 of its 20 references that are in the map.
- First posted on arXiv 7 Oct 2026.

## Builds on (most cited first, 1 of 1)

- [A Secure Beamforming Design: When Fluid Antenna Meets NOMA](https://mlsanity.com/paper/2411.08386/) (2024)

## Abstract (arXiv)

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.