Start Submission Become a Reviewer

Reading: The Astringency of the GP Algorithm for Forecasting Software Failure Data Series

Download

A- A+
dyslexia friendly

Research Papers

The Astringency of the GP Algorithm for Forecasting Software Failure Data Series

Authors:

Yong-qiang Zhang ,

The Information and Electricity-Engineering Institute, Hebei University of Engineering, Handan 056038, China
X close

Hua-shan Chen

The Information and Electricity-Engineering Institute, Hebei University of Engineering, Handan 056038, China
X close

Abstract

The forecasting of software failure data series by Genetic Programming (GP) can be realized without any assumptions before modeling. This discovery has transformed traditional statistical modeling methods as well as improved consistency for model applicability. The individuals' different characteristics during the evolution of generations, which are randomly changeable, are treated as Markov random processes. This paper also proposes that a GP algorithm with "optimal individuals reserved strategy" is the best solution to this problem, and therefore the adaptive individuals finally will be evolved. This will allow practical applications in software reliability modeling analysis and forecasting for failure behaviors. Moreover it can verify the feasibility and availability of the GP algorithm, which is applied to software failure data series forecasting on a theoretical basis. The results show that the GP algorithm is the best solution for software failure behaviors in a variety of disciplines.
DOI: http://doi.org/10.2481/dsj.6.S310
How to Cite: Zhang, Y.-. qiang . & Chen, H.-. shan ., (2007). The Astringency of the GP Algorithm for Forecasting Software Failure Data Series. Data Science Journal. 6, pp.S310–S316. DOI: http://doi.org/10.2481/dsj.6.S310
2
Views
2
Downloads
Published on 23 May 2007.
Peer Reviewed

Downloads

  • PDF (EN)

    comments powered by Disqus