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Research Article
 

Test-sheet Composition Using Cellular Genetic Algorithm with an Improved Evolutionary Rule



Ankun Huang, Dongmei Li and Kuan Lu
 
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ABSTRACT

For an intelligent test-sheet composition system, compared with the traditional test-sheet composition using genetic algorithm, cellular genetic algorithm can significantly improve the convergence velocity and further improve the convergence in the process. However, in the process of mutating, both the diversity index of cellular population and the possibility of escaping from local-best will be decreased. Therefore, this study proposes a cellular genetic algorithm with an improved evolutionary rule applied in the process of test-sheet composition. Experimental results show that the convergence velocity is improved and the speed of decreasing diversity index of cellular population is delayed.

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  How to cite this article:

Ankun Huang, Dongmei Li and Kuan Lu, 2013. Test-sheet Composition Using Cellular Genetic Algorithm with an Improved Evolutionary Rule. Information Technology Journal, 12: 7616-7620.

DOI: 10.3923/itj.2013.7616.7620

URL: https://scialert.net/abstract/?doi=itj.2013.7616.7620
 

REFERENCES

1:  Nebro, A.J., J.J. Durillo, F. Luna, B. Dorronsoro and E. Alba, 2009. MOCell: A cellular genetic algorithm for multiobjective optimization. Int. J. Intell. Syst., 7: 726-746.
CrossRef  |  Direct Link  |  

2:  Canyurt, O.E. and P. Hajela, 2010. Cellular genetic algorithm technique for the multicriterion design optimization. Struct. Multidiscip. Optim., 40: 201-214.
CrossRef  |  

3:  Holland, J.H., 1975. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. 8th Edn., University of Michigan Press, Ann Arbor, IM., USA., ISBN: 9780472084609, Pages: 185

4:  Kamkar, I., M.T. Poostchi and R.A.T. Mohammad, 2010. A cellular genetic algorithm for solving the vehicle routing problem with time windows. Adv. Intell. Soft Comput., 75: 263-270.
CrossRef  |  

5:  Kari, J., 2005. Theory of cellular automata: A survey. Theor. Comput. Sci., 334: 3-33.
CrossRef  |  Direct Link  |  

6:  Kirley, M., X.D. Li and G.D. Green, 1999. Investigation of a cellular algorithm that mimics landscape ecology. Proceedings of the 2nd Asia-Pacific Conference on Simulated Evolution and Learning, Canberra, Australia, November 24-27, 1998, Springer, Berlin, Heidelberg, pp: 90-97

7:  Lin, H.Y., J.M. Su and S.S. Tseng, 2012. An adaptive test sheet generation mechanism using genetic algorithm. Math. PProblems Eng., Vol. 2012.
CrossRef  |  

8:  Liu, Y.F. and S.Y. Liu, 2010. Algorithm based on genetic algorithm for sudoku puzzles. Comput. Sci., 37: 225-226.
CrossRef  |  

9:  Lu, K., D.M. Li, J. Yu and J.X. Wang, 2013. An intelligent test paper construction method based on cellular genetic algorithm. Comput. Eng. Appl., 49: 57-60.

10:  Lu, Y.H. and H. Liu, 2005. Auto-generating examination papers based on integer coding and adaptive genetic algorithm. Comput. Eng., 23: 232-233.

11:  Lu, Y.M., M. Li and L. Li, 2010. The cellular genetic algorithm with evolutionary rule. Chin. J. Electron., 38: 1603-1607.

12:  Song, W. and Q. Liu, 2008. Business process mining based on simulated annealing. Chin. J. Electron., 36: 135-139.

13:  Xiao, G.X., W.C. Zhao, W. Zhu and J.H. Zheng, 2012. Iterant problems replacement method based on genetic algorithm with test paper intelligent generation. Comput. Eng., 38: 150-152.
CrossRef  |  

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