Intelligent Test Paper Composition Based on Improved Adaptive Genetic Algorithm

Online examinations are progressing towards intelligence, with intelligent test paper generation being pivotal in this evolution. The crux of test paper generation lies in multi-objective optimization, where genetic algorithms exhibit distinct advantages in addressing such issues, thereby forming the cornerstone of intelligent test paper generation. Nevertheless, conventional adaptive genetic algorithms encounter challenges of slow convergence and susceptibility to local optima when applied to test paper generation. Hence, a novel intelligent test paper generation method based on an enhanced adaptive genetic algorithm is proposed, significantly enhancing the optimization capability and speed. Ultimately, comparative experiments validate the superiority of this method.