Surrogate models have been widely used in engineering design because of their capability to approximate computationally complex engineering systems. In practice, the choice of surrogate models is extremely important since there are many types of surrogate models, and they also have different hyper-parameters. Traditional manual selection approaches are very time-consuming and cannot be generalized. To address these challenges, an evolutionary algorithm (EA)-based approaches are proposed and studied. However, they lack interpretability and are computationally expensive. To address these gaps, we create a rule-based method for an automatic surrogate model selection called AutoSM. The drastic increase in the selection pace by pre-screening of surrogate model types based on selection rule extraction is the scientific contribution of our proposed method. First, an interpretable decision tree is built to map four critical features, including problem scale, noise, size of sample and nonlinearity, to the types of surrogate model and select the promising surrogate model; then, a genetic algorithm (GA) is used to find the appropriate hyper-parameters for each selected surrogate model. The AutoSM is tested with three theoretical problems and two engineering problems, including a hot rod rolling and a blowpipe design problem. According to the empirical results, using the proposed AutoSM, we can find the promising surrogate model and associated hyper-parameter in 9 times less than other automatic selection approaches such as concurrent surrogate model selection (COSMOS) while maintaining the same accuracy and robustness in surrogate model selection. Besides, the proposed AutoSM, unlike previous EA-based automatic surrogate model selection methods, is not a black box and is interpretable.

替代模型由于能够近似计算复杂的工程系统而已被广泛用于工程设计中。实际上，代理模型的选择非常重要，因为代理模型的类型很多，并且它们也有不同的超参数。传统的手动选择方法非常耗时，不能一概而论。为了解决这些挑战，提出并研究了基于进化算法（EA）的方法。但是，它们缺乏可解释性，并且计算量大。为了解决这些差距，我们为自动代理模型选择创建了一个基于规则的方法，称为AutoSM。通过基于选择规则提取的替代模型类型的预筛选，极大地提高了选择速度，这是我们提出的方法的科学贡献。首先，建立一个可解释的决策树，以将四个关键特征（包括问题规模，噪声，样本大小和非线性）映射到代理模型的类型并选择有前途的代理模型；然后，使用遗传算法（GA）为每个选定的替代模型找到合适的超参数。测试了AutoSM的三个理论问题和两个工程问题，包括热棒轧制和吹管设计问题。根据经验结果，使用建议的AutoSM，与其他自动选择方法（例如并发代理模型选择（COSMOS））相比，我们可以找到有希望的代理模型和相关的超参数减少9倍，同时在代理模型选择中保持相同的准确性和鲁棒性。此外，与以前的基于EA的自动代理模型选择方法不同，拟议的AutoSM不是黑匣子，而且可以解释。