A statistical method of graduates' employment direction based on integrated genetic algorithm

Abstract

In recent years, genetic algorithm is one of the most popular models in artificial intelligence area. It has been widely applied in different fields such as bioinformatics problem solving, traffic engineering, and etc. However, the principles of genetic algorithm are not a complete disclosure to all of us. Therefore, this paper aims to discuss a statistical method of graduates' employment direction based on integrated genetic algorithm. First differentiation of various factors such as academic performance, life experience, industry experience and education level serves as a foundation for obtaining data which is necessary for training and evaluating the model parameters during generation process in this research work. Secondly, a method is proposed to evaluate the model accuracy. Thirdly, for a specific application case of integrated genetic algorithm based method in the graduation employment direction of Zhejiang University, an analysis and comparison with traditional methods are brought forward. The results show that compared with traditional methods, the integrated genetic algorithm based method can effectively improve the accuracy of prediction.