The time complexity analysis of a class of gene expression programming

Xin Du, Youcong Ni, Datong Xie, Xin Yao, Peng Ye, Ruliang Xiao

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


This paper studies the time complexity of gene expression programming based on maintaining elitist (ME-GEP). Using the theory of Markov chain and the technique of artificial fitness level, the properties of transition matrices of ME-GEP are analyzed. Based on the properties, the upper and lower bounds of the average time complexity of ME-GEP are obtained. Furthermore, the upper bound is estimated, which is determined by the parameters of ME-GEP algorithm. And the theoretical results acquired in this paper are used to analyze ME-GEP for solving function modeling and clustering problem. At last, a set of experiments are performed on these problems to illustrate the effectiveness of theoretical results. The results show that the upper bound of expected first hitting time can be used to direct the algorithm design of ME-GEP.
Original languageEnglish
Pages (from-to)1611-1625
Number of pages15
JournalSoft Computing
Issue number6
Early online date13 Dec 2014
Publication statusPublished - 1 Jun 2015


  • GEP
  • ME-GEP
  • Average time complexity
  • Artificial fitness level
  • Markov chain


Dive into the research topics of 'The time complexity analysis of a class of gene expression programming'. Together they form a unique fingerprint.

Cite this