Research on the Application of Genetic Algorithm in Physical Education

• Published in: Journal of Mathematics Vol. 2022; no. 1
• Main Author: Wang, Haibo
• Format: Journal Article
• Language: English
• Published: Cairo Hindawi 2022; John Wiley & Sons, Inc; Wiley
• Subjects: Algorithms; Colleges & universities; Convergence; Curricula; Education; Genetic algorithms; Learning; Mathematical models; Mathematics; Methods; Online instruction; Optimization; Physical education; Physical education and training; School environment; Skills; Sports; Students; Teaching methods; China
• DOI: 10.1155/2022/8477945
• ISSN: 2314-4629

University physical education is an important public basic course in colleges and universities. The traditional teaching is usually within the class time specified in the training program; the teacher teaches the students the basic physical education fundamentals so that the students can master the basic skills of sports, thus improving the students’ sports level and physical quality. An improved genetic algorithm is proposed to reduce the problem of slow convergence and partial convergence of the fundamental genetic algorithm for intelligent grouping systems. To ensure the group’s stability and variety, the algorithm can rapidly extend the search space by repeatedly rejecting similar individuals. Therefore, this study proposes a new method of intelligent grouping based on the improved genetic algorithm. The new method can overcome the problem of premature convergence of the algorithm more efficiently and easily than the traditional algorithm. A large number of experiments have proved that the proposed algorithm meets all the requirements of physical education very well. The algorithm can automatically generate test papers with moderate difficulty and reasonable structure.