EAAI Journal 2026 Journal Article
A zero-shot tree-structured multi-objective evolutionary Neural Architecture Search
- Yan Dai
- Lixin Wei
- Ziyu Hu
- Hao Sun
- Qianao Xu
- Kexin Zhang
- Boya Zhao
Neural Architecture Search (NAS) enables the automated design of high-performance neural networks; however, its practical application is often constrained by substantial computational costs, the limited reliability of single-objective proxy metrics, and insufficient modeling of architectural information flow. To address these limitations, we propose Tree-structured Evolutionary Neural Architecture Search (TreeNAS). The method integrates three components: (1) a tree-structured encoding with refinement to preserve backbone information paths under mutation; (2) a zero-cost multi-objective evaluation that jointly assesses trainability, generalization, and complexity, thereby mitigating instability from single-objective proxy; and (3) a Pareto-dominance-guided evolutionary search to encourage diverse, balanced architectures across objectives. On the standard Neural Architecture Search Benchmark 101 (NAS-Bench-101) and Neural Architecture Search Benchmark 201 (NAS-Bench-201) datasets, TreeNAS achieves state-of-the-art accuracy with a 40 × reduction in search cost. On the ImageNet dataset under strict floating-point operation (FLOPs) budgets, TreeNAS achieves accuracy comparable to training-based NAS methods while keeping the search cost to 0. 45 Graphics Processing Unit (GPU) days. Additionally, TreeNAS generalizes across modalities, from two-dimensional images to medical signals and volumetric imaging, demonstrating its potential in practical medical imaging applications.