Test Paper Generation Based on Improved Genetic Simulated Annealing Algorithm

With the wide application of online examinations in colleges and universities, intelligent test paper generation technology has become a hot topic. In this paper, we propose an improved genetic simulated annealing algorithm (GSA) and apply it to the test paper generation process to improve the quality and speed of intelligent test paper generation. Firstly, we optimize the genetic operators in real number coding and introduce an adaptive adjustment strategy on the premise of maintaining individual diversity. Secondly, we apply simulated annealing to individuals of each generation. Finally, we conduct abundant experiments. The results show that the improved algorithm can promote the success rate of test paper generation and effectively enhance the quality of test paper.