LLMENAS: Evolutionary Neural Architecture Search via Large Language Model Guidance
提出LLMENAS框架，利用大语言模型作为适应度设计器，动态调整搜索方向以跳出局部最优，在CIFAR-10、CIFAR-100和ImageNet上取得高精度且搜索高效。
Differentiable Neural Architecture Search (NAS) and traditional evolutionary approaches frequently struggle with premature convergence to local optima. To overcome this limitation, we propose LLMENAS, a hierarchical framework that introduces trajectory-aware fitness design as the upper-level optimizer. By analyzing the convergence state of historical optimization trajectories, the Large Language Model (LLM) acts as a fitness designer to dynamically design fitness functions. This mechanism enables the search to navigate complex landscapes and escape local optima. Furthermore, we introduce a closed-loop self-improving mechanism, enabling the LLM to iteratively enhance its design strategies through self-reflection and self-refinement based on feedback. Extensive experiments show that LLMENAS achieves competitive results, with top-1 accuracies of 97.58% on CIFAR-10, 83.52% on CIFAR-100, and 75.6% on ImageNet-1k. Furthermore, it is achieved with remarkable efficiency, costing only 0.15 GPU days on the CIFAR 10 datasets and 2 GPU days on ImageNet. The source code is publicly available at: https://github.com/LLMENAS/LLMENAS.