Model Selection for Gasoline Direct Injection Characteristics Using Boosting and Genetic Algorithms

Abstract

New emission regulations, the demand of high power output and high efficiency of Gasoline Direct Injection (GDI) engines led to an intense development of new tools and approaches in the study of combustion processes. The generation of the data in this context through simulations and measurements is however an expensive and time-consuming process. Therefore, novel Machine Learning methods can be applied to support GDI developments. In the current paper, an innovative approach regarding the analysis of GDI related data is proposed. Specifically, Extreme Gradient Boosting Machine is chosen due to its high efficiency and powerful feature analysis coming along during the models training. In addition, a parameter-free, fast and dynamic data-driven model selection method is presented. This includes the genetic algorithm NSGA-II to identify the best set of hyperparameters by means of good generalization and precision. The potential of the proposed method is finally demonstrated on real-world data coming from the GDI development field and public data compared with state-of-the-art approaches.