ReEvo-designed memetic algorithm for secure fluid antenna systems
An LLM evolves crossover, mutation and local-search operators offline against a wireless simulation, and the finished algorithm runs without any LLM.
The loop
- Proposes
Crossover, mutation and local-search operator code for fluid antenna port selection, written by DeepSeek Reasoner
- Judged by
Best secure sum-rate an inner genetic algorithm reaches with the operators on training channels, via secure WMMSE
- Steers
ReEvo's reflective evolution feeds performance comparisons back into the next round of operator code
Headline result
At equal generation counts (100 generations), the evolved memetic algorithm reached 11.423 bit/s/Hz at 1 W (7.6% over a conventional GA) and 23.611 bit/s/Hz at 5 W (8.2% over a conventional GA), and about 4 to 6% above the two LLM-assisted GA baselines.
What this does not prove
Equal generations are not equal evaluation budgets, because local search adds evaluations, and error bars overlap with the LLM-GA baselines at some operating points. Results are simulated (10 users, one eavesdropper, 20x20 grid) in a preprint accepted by WCSP 2026.
How it generalizes the harness
The LLM works only offline to design the search operators, and the deployed memetic algorithm needs no LLM queries.
Classification
| What it proposes | Search operator |
|---|---|
| Generative model | Pretrained LLM (DeepSeek Reasoner) inside the ReEvo reflective-evolution framework |
| Algorithmic authority | Wireless channel simulation with secure WMMSE beamforming scoring each port subset |
| Feedback | Written reflection |
| What gets updated | Population of candidates |
| When it runs | Offline, at design time |
| Validity | Empirical (measured) |
| Evidence | Experiments reported |
| Lineage | Application |