Beyond LLM-GA: Secure Fluid Antenna Systems with ReEvo-Designed Memetic Algorithm
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
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arXiv:2606. 15788v1 Announce Type: cross Abstract: Large Language Models (LLMs) constitute pivotal components within the AI-dominated information technology ecosystem.
arXiv:2607. 08791v1 Announce Type: cross Abstract: Designing effective multi-objective Bayesian optimization (MOBO) algorithms requires balancing many interdependent design choices whose optimal configuration is problem-dependent and typically demands deep expertise.
arXiv:2607. 17762v1 Announce Type: cross Abstract: Large language model (LLM)-driven evolutionary search is an emerging algorithm-discovery paradigm that has already produced novel results in several scientific fields.
The paper presents a hybrid approach that combines large language models (LLMs) with genetic algorithms to solve ARC-AGI-2 tasks. An LLM (Qwen3.5-4B) first generates a small set of programs, which seed a genetic algorithm that evolves these programs within a domain‑specific language ensuring validity. This method yields 6 correct solutions out of 60 tasks (10%), outperforming either technique alone.
arXiv:2606. 14202v1 Announce Type: cross Abstract: Large Language Models (LLMs) have advanced Automatic Heuristic Design (AHD) by enabling heuristic generation through reasoning and code synthesis.
arXiv:2505.15741v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) and Evolutionary Computation (EC) are increasingly being combined to support automated optimization, algorithm d...