Modelling and Simulation of Intelligent English Paper Generating Based on SSA-GA

Author:

Han Limin1ORCID,Gao Hong2ORCID,Zhai Rongjie2ORCID

Affiliation:

1. School of Foreign Studies, Anhui Polytechnic University, Wuhu 241000, China

2. School of Artificial Intelligence, Anhui Polytechnic University, Wuhu 241000, China

Abstract

To enhance the quality and efficiency of computer-enabled generation of papers for Test for English Majors Band 8 (TEM-8), a paper generation model supported by sparrow search algorithm-genetic algorithm was studied. First, a simplified test paper generation mathematical model was set up after analyzing and studying types and characteristics of TEM-8 tasks. In the model, quantity, type, difficulty, discrimination degree, scores, exposure, and answering time of test questions were taken into consideration. To enhance the optimizing effect of the genetic algorithm for searching test questions, the traditional genetic algorithm was improved by introducing the sparrow search algorithm into the model to achieve a better crossover rate, variance rate, optimization precision, and speed of the genetic algorithm. A new sparrow search-genetic algorithm (SSA-GA) was designed, and the optimizing effect of SSA-GA was verified to be ideal through optimizing six standard test functions. Then, SSA-GA was applied to conduct experimentation with test paper generation, and comparison with traditional genetic algorithms was also made. The values of best and average fitness of SSA-GA were better than those of the traditional genetic algorithm (GA) in the paper generation. Exposure rate and success rate in TEM-8 paper generation of SSA-GA were higher than those of traditional GA in TEM-8 paper generation. Results showed that the studied SSA-GA could implement test paper generation with higher speed and better quality.

Funder

Key Research Project of Humanities and Social Science of Universities in Anhui Province

Publisher

Hindawi Limited

Subject

General Engineering,General Mathematics

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