Strategy and Applied Research of Multi-Constrained Model of Automatic Test Paper Based on Genetic Algorithm

Zhi Feng Liu; Ji Shi; Jia Liu; Yang Li

doi:10.4028/www.scientific.net/AMM.37-38.1223

Paper Titles

Sixth Drawing Process Analysis for Electrodeposited Nickel Coating Battery Shells
p.1206

Solar Cell Surface Texturing Combined Wet Etching and Ultrasonic Vibration
p.1210

Some Properties of Mathematical Model for Cylindricity Errors
p.1214

Stochastic Optimal Dispatch of Power System under Extreme Weather Disaster
p.1219

Strategy and Applied Research of Multi-Constrained Model of Automatic Test Paper Based on Genetic Algorithm
p.1223

Strategy for Developing Enterprise Cluster by Means of Establishing Regional Commercial Alliance
p.1231

Structural Analysis of Agricultural Truck Frame Based on the Finite Element Method
p.1236

Structural Design of Hoist Boom of Bracing-Hoisting-Separated Barrier-Clearing Vehicle Based on ANSYS
p.1240

Structural Intensity Analysis of Shells Structure under Harmonic Excitation
p.1245

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 37-38Strategy and Applied Research of Multi-Constrained...

Strategy and Applied Research of Multi-Constrained Model of Automatic Test Paper Based on Genetic Algorithm

Abstract:

Test paper problem is a typical multi-constrained objective optimization problem. By using genetic algorithm, this paper analyzes the initial population generation, the chromosome coding and its genetic manipulation, control parameters. Solving that by natural-coded genetic algorithm, improves test paper success rate and convergence rate. This genetic algorithm is applied successfully on NHibernate architecture, and developed "automatic test paper" Online Examination system.

You might also be interested in these eBooks

Advances in Engineering Design and Optimization

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 37-38)

Pages:

1223-1230

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.37-38.1223

Citation:

Cite this paper

Online since:

November 2010

Authors:

Zhi Feng Liu, Ji Shi, Jia Liu, Yang Li

Keywords:

Genetic Algorithm (GA), Multi-Constraint, NHibernate Architecture, Objective Optimization, Test Paper Generation System

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Y.R. Wang, Z. Zhang and Y.X. Shi: Journal of University of Electronic Science and Technology of China, Vol. 35 (2006) No. 3, pp.363-366.

Google Scholar

[2] Y.H. Lu and H. Liu: Computer Engineering, Vol. 31 (2005) No. 1, pp.232-233.

Google Scholar

[3] S.P. Wang and Y.Q. Yi: Science Technology and Engineering, Vol. 6 (2006) No. 4, pp.468-470.

Google Scholar

[4] Y.Y. Wang, S. Hou and M.Z. Guo: Journal of Harbin Institute of Technology, Vol. 35 (2005) No. 3, pp.342-346.

Google Scholar

[5] H.Y. Quan: Wuhan University Journal of Natural Sciences, Vol. 45 (2009) No. 5, pp.758-760.

Google Scholar

[6] G.L. Chen: Genetic Algorithm and Application (National Defense Press, China 2007).

Google Scholar

[7] T.N. Bui and T.V.H. Nguyen: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation (Seattle, USA, July 8-12, 2006), Vol. 1, pp.19-26.

Google Scholar

[8] T.N. Bui and T.V.H. Nguyen: Discrete Applied Mathematics, Vol. 156 (2008) No. 2, pp.190-200.

Google Scholar

[9] M.E. Mezura and A.C.C. Carlos: IEEE Trans on Evolutionary Computation, Vol. 9 (2005) No. 1, pp.1-17.

Google Scholar

[10] L. Costa and P. Oliveira: Proceedings of the 2002 Congress on Evolutionary Computation (Honolulut, USA, 2002), Vol. 1, pp.97-102.

Google Scholar