Abstract: Online exams abandon the inherent shortcomings of traditional paper exams and have been widely used in the field of online education. The test paper of artificial intelligence is one of the important techniques for completing online examinations efficiently. The question of test paper is a multi-development goal combination and optimization problem, and generally has several solutions. Artificial intelligence algorithms have obvious advantages in finding the optimal solution of such problems. This paper first analyzes and studies the current mainstream intelligent test paper generation algorithm, combines the relevant principles of test paper generation and mathematical experiment models, and proposes an intelligent test paper generation strategy based on particle swarm genetic algorithm. The particles, individual extremes in the population and the extremes of the population are performed the crossover operation in the genetic algorithm and the mutation operation of the particle itself. At the same time, the algorithm performance is improved by adaptively adjusting the crossover probability and the mutation probability, and the segmented real number encoding. Finally, a comparative experiment is taken to prove the advantages of the algorithm.

Key words: online examination, test paper generation theory, mathematical model, PSO-GA, encoding