JSW 2012 Vol.7(1): 243-248 ISSN: 1796-217X
doi: 10.4304/jsw.7.1.243-248
doi: 10.4304/jsw.7.1.243-248
BS-GEP Algorithm for Prediction of Software Failure Series
Yongqiang ZHANG, Jing XIAO, and Shengjuan SUN
Hebei University of Engineering/ The information and electricity-engineering institute, Handan, China
Abstract—This paper introduces GEP(Gene Expression Programming) fundamental. Aimed at prediction of software failure sequence, an improved GEP(GEP based on Block Strategy, BS-GEP) is presented, in which the population is divided into several blocks according to the individual fitness of each generation and the genetic operators are reset differently in each block to guarantee the genetic diversity. The algorithm complexity and convergence of BS-GEP is analyzed in the paper. Furthermore, BS-GEP is applied in the solution of prediction in software failure sequence. The simulation results show that the model found by BS-GEP, which is proved widely used for many other time series, is more accurate than the one of classic GEP.
Index Terms—BS-GEP; Complexity Analysis; Convergence Analysis; Software Failure; Time Series Prediction
Abstract—This paper introduces GEP(Gene Expression Programming) fundamental. Aimed at prediction of software failure sequence, an improved GEP(GEP based on Block Strategy, BS-GEP) is presented, in which the population is divided into several blocks according to the individual fitness of each generation and the genetic operators are reset differently in each block to guarantee the genetic diversity. The algorithm complexity and convergence of BS-GEP is analyzed in the paper. Furthermore, BS-GEP is applied in the solution of prediction in software failure sequence. The simulation results show that the model found by BS-GEP, which is proved widely used for many other time series, is more accurate than the one of classic GEP.
Index Terms—BS-GEP; Complexity Analysis; Convergence Analysis; Software Failure; Time Series Prediction
Cite:Yongqiang ZHANG, Jing XIAO, and Shengjuan SUN, "BS-GEP Algorithm for Prediction of Software Failure Series," Journal of Software vol. 7, no.1, pp. 243-248, 2012.
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ISSN: 1796-217X (Online)
Frequency: Quarterly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
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