DocumentCode
2304012
Title
Notice of Retraction
Automatic Generating Test Paper System Based on Genetic Algorithm
Author
Lirong Xiong ; Jianwei Shi
Author_Institution
Coll. of Comput. Sci. & Technol., Zhejiang Univ. of Technol., Hangzhou, China
Volume
3
fYear
2010
fDate
6-7 March 2010
Firstpage
272
Lastpage
275
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Traditional test paper generating algorithms have some shortcomings, such as slow generating speed, low success probability and low generating quality. The population search strategy in Genetic Algorithm (GA) provides a very suitable solution for multi-objective optimization, so applying it to the issue of test paper auto-generating can achieve good results. In this paper, we study the issue of test paper generation and propose the mathematical model and object function. Then some improvements are made in Simple GA for test paper generation. Based on the algorithm, a system is designed and implemented.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Traditional test paper generating algorithms have some shortcomings, such as slow generating speed, low success probability and low generating quality. The population search strategy in Genetic Algorithm (GA) provides a very suitable solution for multi-objective optimization, so applying it to the issue of test paper auto-generating can achieve good results. In this paper, we study the issue of test paper generation and propose the mathematical model and object function. Then some improvements are made in Simple GA for test paper generation. Based on the algorithm, a system is designed and implemented.
Keywords
educational administrative data processing; genetic algorithms; search problems; automatic generating test paper system; genetic algorithm; mathematical model; multiobjective optimization; object function; population search strategy; Automatic testing; Biological cells; Computer science; Convergence; Educational institutions; Educational technology; Genetic algorithms; Mathematical model; Paper technology; System testing; Genetic Algorithm; constraints; mathematical model; test paper auto-generating;
fLanguage
English
Publisher
ieee
Conference_Titel
Education Technology and Computer Science (ETCS), 2010 Second International Workshop on
Conference_Location
Wuhan
Print_ISBN
978-1-4244-6388-6
Type
conf
DOI
10.1109/ETCS.2010.250
Filename
5460073
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