Abstract
Neural architecture search (NAS) is an attractive approach to automate the design of optimized architectures but is constrained by high computational budget, especially when optimizing for multiple, important conflicting objectives. To address this, an adaptive Co-Kriging-assisted multi-fidelity multi-objective NAS algorithm is proposed to further reduce the computational cost of NAS by incorporating a clustering-based local multi-fidelity infill sampling strategy, enabling efficient exploration of the search space for faster convergence. This algorithm is further accelerated by the use of a novel continuous encoding method to represent the connections of nodes in each cell within a generalized cell-based U-Net backbone, thereby decreasing the search dimension (number of variables). Results indicate that the proposed NAS algorithm outperforms previously published state-of-the-art methods under limited computational budget on three numerical benchmarks, a 2D Darcy flow regression problem and a CHASE_DB1 biomedical image segmentation problem. The proposed method is subsequently used to create a wind velocity regression model with application in urban modelling, with the found model able to achieve good prediction with less computational complexity. Further analysis revealed that the NAS algorithm independently identified principles undergirding superior U-Net architectures in other literature, such as the importance of allowing each cell to incorporate information from prior cells.
Abstract (translated)
神经架构搜索（NAS）是一种自动设计优化网络结构的方法，但其计算成本高昂，尤其是在针对多个重要且相互冲突的目标进行优化时。为此，提出了一种基于自适应Co-Kriging的多保真度多目标NAS算法，通过引入一种基于聚类的局部多保真度填充采样策略来进一步降低NAS的计算成本，从而能够有效地探索搜索空间并加速收敛过程。此外，该算法还采用了一种新颖的连续编码方法表示每个单元内的节点连接，在一个通用的基于单元的U-Net骨干网络中减少了搜索维度（变量的数量）。实验结果表明，在三个数值基准、二维达西流回归问题以及CHASE_DB1生物医学图像分割问题上，在有限计算预算下，所提出的NAS算法优于先前公布的最先进方法。此外，该方法还被用于创建一个应用于城市建模的风速回归模型，找到的模型能够在降低计算复杂度的情况下实现良好的预测效果。进一步分析表明，NAS算法独立地识别出了其他文献中优越U-Net架构背后的原则，例如允许每个单元从先前单元中获取信息的重要性。
URL
https://arxiv.org/abs/2509.01943
