Virus Evolution Based Gene Expression Programming for Classification Rules Mining

Wei Hong Wang; Yan Ye Du; Qu Li

doi:10.4028/www.scientific.net/KEM.467-469.1392

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HomeKey Engineering MaterialsKey Engineering Materials Vols. 467-469Virus Evolution Based Gene Expression Programming...

Virus Evolution Based Gene Expression Programming for Classification Rules Mining

Abstract:

Gene Expression Programming(GEP) is a novel and accurate approach for classification. With the shortcoming of GEP, it often falls into the local optimums. In this paper, we introduce the virus evolutionary mechanism into GEP, with the infection operation of virus population, the diversity of the host population is increased, and the system is much easier to jump out of the local optimums, and much faster to obtain better results. Experiments on several benchmark data sets show that our approach can get close average accuracy and much better best accuracy compared with available results. What’s more, the average execution time is largely decreased due to smaller population size and maximum generation.

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Periodical:

Key Engineering Materials (Volumes 467-469)

Pages:

1392-1397

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.467-469.1392

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Online since:

February 2011

Authors:

Wei Hong Wang, Yan Ye Du, Qu Li

Keywords:

Classification, Gene Expression Programming, Virus Evolution

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References

[1] C. Ferreira, Gene Expression Programming: A New Adaptive Algorithm for Solving Problem , Complex Systems, vol. 13, issue 2, 2001, p.87–129.

Google Scholar

[2] Cao Xianbin, Wang Bennian and Wang Xufa, A Virus Evolutionary Genetic Algorithm, Journal of Mini-micro Systems, 2001, (01).

Google Scholar

[3] Naoyuki K, Koji S, Fukuda T. The role of virus infection in virus-evolutionary genetic algorithm. In: Proc. of the IEEE Int'l. Conf . on Evolutionary Computation. Nagoya: IEEE, 1996. 182~187.

DOI: 10.1109/icec.1996.542357

Google Scholar

[4] Hu Shicheng, Xu Xiaofei and Li Xiangyang, A Virus Co-evolution Genetic Algorithm for Project Optimization Scheduling, Journal of Software, 2004, (01).

Google Scholar

[5] Hu Shicheng, Xu Xiaofei and Zhan Dechen, A Virus Evolutionary Genetic Algorithm for Large Product Structure Optimization Problem, Journal of Computer Integrated Manufacturing Systems, 2003, (03).

Google Scholar

[6] C. Zhou, W. Xiao, T. M. Tirpak, and P. C. Nelson, Evolving Accurate and Compact Classification Rules With Gene Expression Programming, IEEE Transactions on Evolutionary Computation, vol. 7, No. 6, December 2003, p.519–531.

DOI: 10.1109/tevc.2003.819261

Google Scholar

[7] C. Blake, E. Keogh, and C. J. Merz. (1998) UCI Repository of Machine Learning Databases. Univ. California at Irvine, Dept. Inform. Comput. Sci., CA. [Online]. Available: http: /www. ics. uci. edu/~mlearn/ MLRepository. html.

Google Scholar