Transformer-Guided Swarm Intelligence for Frugal Neural Architecture Search
Neural Architecture Search (NAS) has automated the design of deep learning models but traditionally requires massive computational resources, often measured in thousands of GPU-days. In this paper, we propose a frugal and memetic NAS framework designed to democratize architecture design on consumer-grade hardware. Our approach combines the global macro-search capabilities of an autoregressive Transformer controller, trained via Reinforcement Learning (RL), with the local micro-exploitation of an Artificial Bee Colony (ABC) algorithm. To prevent premature convergence during the RL phase, we introduce a dynamic entropy mechanism that forces topological exploration upon detection of performa...
| Authors | Romain Amigon |
|---|---|
| Primary category | cs.LG |
| Published | 13 July 2026 |
| Last updated at source | 13 July 2026 |
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