The use of surrogate models to replace expensive computations with computer simulations has been widely studied in engineering problems. However, often only limited simulation data is available when designing complex products due to the cost of obtaining this kind of data. This presents a challenge for building surrogate models because the information contained in the limited simulation data is incomplete. Therefore, a method for building surrogate models by integrating limited simulation data and engineering knowledge with evolutionary neural networks (eDaKnow) is presented. In eDaKnow, a neural network uses an evolutionary algorithm to integrate the simulation data and the monotonic engineering knowledge to learn its weights and structure synchronously. This method involves converting both limited simulation data and engineering knowledge into the respective fitness functions. Compared with the previous work of others, we propose a method to train the surrogate model by combining data and knowledge through evolutionary neural network. We take knowledge as fitness function to train the model, and use a network structure self-learning method, which means that there is no need to adjust the network structure manually. The empirical results show that: (1) eDaKnow can be used to integrate limited simulation data and monotonic knowledge into a neural network, (2) the prediction accuracy of the newly constructed surrogate model is increased significantly, and (3) the proposed eDaKnow outperforms other methods on relatively complex benchmark functions and engineering problems.

在工程问题中，已经广泛研究了使用替代模型以计算机模拟代替昂贵的计算。然而，由于获取此类数据的成本，在设计复杂产品时通常只能获得有限的仿真数据。这对构建代理模型提出了挑战，因为有限的模拟数据中包含的信息不完整。因此，通过构建替代模型的方法积分用有限的模拟DA TA和工程知识窗台与è提出了进化神经网络（eDaKnow）。在 eDaKnow 中，神经网络使用进化算法将仿真数据和单调工程知识结合起来，同步学习其权重和结构。该方法涉及将有限的模拟数据和工程知识转换为各自的适应度函数。与其他人之前的工作相比，我们提出了一种通过进化神经网络结合数据和知识来训练代理模型的方法。我们将知识作为适应度函数来训练模型，并使用网络结构自学习的方法，这意味着不需要手动调整网络结构。实证结果表明：（1）eDaKnow可以将有限的仿真数据和单调知识整合到一个神经网络中，