Evolutionary Zero-Shot Proxy in Various Evaluation Scenarios: A Symbolic Learning Perspective
将零样本代理视为架构特征，用遗传编程自动进化出可解释的代理组合，在点式、成对和列表式三种评估场景下提升排序相关性，适用于神经架构搜索中的高效评估。
Neural architecture search (NAS) faces a significant challenge due to the high computational cost of architecture evaluation. Zero-shot (ZS) proxies have been proposed as an efficient alternative to reduce evaluation overhead; however, their limited rank correlation often leads to sub-optimal architecture selection. To alleviate this issue, recent studies attempt to combine multiple ZS proxies, yet most existing approaches rely on heuristic aggregation guided by domain knowledge and remain restricted to a single evaluation scenario, which limits both effectiveness and interpretability. In this paper, we propose a novel perspective that treats ZS proxies as architectural features and formulates their combination as an evolutionary symbolic learning problem. Specifically, we leverage a genetic programming based symbolic learning framework to automatically evolve interpretable proxy combinations, enabling flexible exploration of symbolic structures without manual design. The evolved symbolic models capture complex and non-linear relationships between ZS proxies and architectural performance while maintaining strong interpretability. Furthermore, we systematically model three evaluation scenarios, including pointwise, pairwise, and listwise settings, and design scenario-specific fitness functions to guide evolutionary search toward different ranking objectives. Extensive experiments across 19 tasks using 13 representative ZS proxies are conducted to evaluate the proposed framework. The results demonstrate that the evolved symbolic models consistently achieve improved rank correlation and provide valuable insights into proxy interaction patterns under different evaluation scenarios, highlighting the effectiveness and generalizability of the proposed approach for NAS.