Application of genetic programming on statistical modeling

  • Kang Shun Li*
  • , Yuan Xiang Li
  • , Ming Duan Tang
  • , Ai Min Zhou
  • , Zhi Jian Wu
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

An application of genetic programming in statistical modeling is proposed, which obviously improved the traditional regular methods of statistical modeling that could only obtain rough curve fitting and unsatisfactory results. Then, the estimating standard error and forecasting standard error were calculated and analyzed. By using the actual historical data from Statistics Yearbook of China and Statistics Yearbook of Jiangxi Province, China published in recent years, the automatic generated statistical model of economic forecasting by using genetic programming was established and the result indicates that the accuracy calculated by this statistical model is obviously much higher, compared with traditional methods such as linear regression, exponential regression and parabolic regression[3].

Original languageEnglish
Pages (from-to)1597-1600
Number of pages4
JournalXitong Fangzhen Xuebao / Journal of System Simulation
Volume17
Issue number7
StatePublished - Jul 2005
Externally publishedYes

Keywords

  • Automatic building model
  • Evolutionary computation
  • Regression
  • Statistical forecasting

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