Title :
An Improved Genetic Algorithm for Intelligent Test Paper Generation
Author_Institution :
Dept. of Comput. Sci., GuangDong Coll. of Sci. & Technol., Dongguan, China
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;
Conference_Titel :
Intelligent Computation Technology and Automation (ICICTA), 2014 7th International Conference on
Conference_Location :
Changsha
Print_ISBN :
978-1-4799-6635-6
DOI :
10.1109/ICICTA.2014.25
