Data driven industrial modeling has been comprehensively studied for its high modeling accuracy. However, the unexplainable characteristic of data-driven modeling hinders workers from understanding the model and controlling process, and further holds back its application in industrial process. In order to solve this problem, we propose a genetic algorithm based method to construct interpretable features for industrial modeling in this paper. This model adopts the framework similar to genetic algorithm, but redefines the populations as features to adapt to the task of feature construction. The populations are evaluated by fitness function with punishment term to ensure the constructed features are concise. By using different genetic mutation and crossover operators, the proposed framework has the ability to combine domain knowledge to handle the characteristics of data, such as nonlinear, dynamic and time lag. The proposed method is experimented on the silicon content prediction task in ironmaking process, which is a classical process industry, achieving high accuracy.

由于数据驱动的工业建模具有较高的建模精度，因此已经对其进行了全面的研究。但是，数据驱动建模的无法解释的特征阻碍了工人理解模型和控制过程，从而进一步阻碍了其在工业过程中的应用。为了解决这个问题，本文提出了一种基于遗传算法的可解释特征建模方法。该模型采用类似于遗传算法的框架，但是将种群重新定义为特征，以适应特征构建的任务。使用适应度函数和惩罚项对总体进行评估，以确保构造的特征简洁。通过使用不同的基因突变和交叉算子，所提出的框架具有结合领域知识来处理数据特征（如非线性，动态和时滞）的能力。该方法在炼铁过程中的硅含量预测任务上进行了实验，这是一个经典的过程工业，具有很高的精度。