An Improved Genetic Algorithm for Intelligent Test Paper Generation

Author

Nie Jun

Author_Institution

Dept. of Comput. Sci., GuangDong Coll. of Sci. & Technol., Dongguan, China

fYear

2014

fDate

25-26 Oct. 2014

Firstpage

72

Lastpage

75

Abstract

Considering the problem on generating test papers is multi-objective parameter optimization under multiple constraints. I proposed a new improved genetic algorithm based on the researches of the mathematical model of generating test paper after encoding segmented chromosome, confirming adaptability function, segmented group initialization, altering adaptive crossover probability and mutation probability and conserve optimization individuals. This method implemented generating test paper well, and the experimental results show that this improved genetic algorithm is more practical and effective compared to the common algorithm in the same conditions.

Keywords

computer aided instruction; genetic algorithms; probability; adaptability function; adaptive crossover probability; improved genetic algorithm; intelligent test paper generation; mathematical model; multiobjective parameter optimization; mutation probability; segmented chromosome; Algorithm design and analysis; Biological cells; Encoding; Genetic algorithms; Optimization; Sociology; Statistics; Adaptive; Improved Genetic; Intelligent Test Paper Generation;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Computation Technology and Automation (ICICTA), 2014 7th International Conference on

Conference_Location

Changsha

Print_ISBN

978-1-4799-6635-6

Type

conf

DOI

10.1109/ICICTA.2014.25

Filename

7003488