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Research on Intelligent Test Paper Generation System Based on Improved Genetic Algorithm

Wenming Huang

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Abstract

Studies the mathematical model of test theory and test paper composition,and analyzes applying genetic algorithm to achieve the aim of intelligent test paper generation,which is to find outthe best test paper that can meet the demands of the users.At the meantime,mainly studies the reasons of prematurity phenomenon of simple genetic algorithm,and improves paramedic coding,population initialization,genetic operation and control parameter.The simulation experiment shows that compared with simple genetic algorithm,improved genetic algorithm has faster convergence rate and higher stability.What's more,it can avoid prematurity effectively so as to better accomplish intelligent test paper generation.

About this research paper

What this paper is about

Studies the mathematical model of test theory and test paper composition,and analyzes applying genetic algorithm to achieve the aim of intelligent test paper generation,which is to find outthe best test paper that can meet the demands of the users.At the meantime,mainly studies the reasons of prematurity phenomenon of simple genetic algorithm,and improves paramedic coding,population initialization,genetic operation and control parameter.The simulation experiment shows that compared with simple genetic algorithm,improved genetic algorithm has faster convergence rate and higher stability.What's more,it can avoid prematurity effectively so as to better accomplish intelligent test paper generation.

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Available abstract

Studies the mathematical model of test theory and test paper composition,and analyzes applying genetic algorithm to achieve the aim of intelligent test paper generation,which is to find outthe best test paper that can meet the demands of the users.At the meantime,mainly studies the reasons of prematurity phenomenon of simple genetic algorithm,and improves paramedic coding,population initialization,genetic operation and control parameter.The simulation experiment shows that compared with simple genetic algorithm,improved genetic algorithm has faster convergence rate and higher stability.What's more,it can avoid prematurity effectively so as to better accomplish intelligent test paper generation.

Key concepts: Computer science, Initialization, Genetic algorithm, Population-based incremental learning, Coding (social sciences), Convergence (economics), Simple (philosophy), Algorithm

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