Title :
Research on Intelligent Test Paper Generation Based on Improved Genetic Algorithm
Author :
Liu, Dan ; Zheng, Lijuan ; Wang, Xuejun ; Zhuan, Sunying
Author_Institution :
Sch. of Inf. Sci. & Technol., Shijiazhuang Tiedao Univ., Shijiazhuang, China
Abstract :
In order to avoid slow-convergence and local convergence of simple genetic algorithm (SGA) for intelligent test paper generation, a kind of improved genetic algorithm (IGA)has been proposed in this paper. This algorithm uses unceasing elimination of similar individual method to quickly enlarge the search space and to stabilize the individual diversity of the group. Experiment results show that the test paper formed by the algorithm meets all the user requirements if the quantity of test questions is moderate and reasonable.
Keywords :
education; genetic algorithms; search problems; computer test system; improved genetic algorithm; intelligent test paper; search space; user requirement; Computers; Convergence; Evolution (biology); Hamming distance; Indexes; Testing; improved genetic algorithm; intelligent test paper generation; mathematical model; similar individual;
Conference_Titel :
Intelligent Systems (GCIS), 2010 Second WRI Global Congress on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-9247-3
DOI :
10.1109/GCIS.2010.34