Modelling and Simulation of Intelligent English Paper Generating based on Developed Genetic Algorithm - Limin Han, Aiyun Chen, Rongjie Zhai (Forest Chemicals Review)
In order to solve problems in generating paper for Test for English Majors (TEM) more effectively, a simplified generating model is proposed based on analyzing test features and requirements. Meanwhile, to improve the efficiency and quality of intelligent English paper generating, the normal genetic algorithm (GA) is improved. In addition, butterfly optimization algorithm is introduced to select the crossover rate and mutation rate for the genetic algorithm so that its optimization accuracy and speed are improved. Finally, the developed genetic algorithm is used to conduct the experiments of TEM paper generating and compared with the normal genetic algorithm. The simulation experimental results of generating TEM papers show that the developed genetic algorithm can work quickly with higher quality.