caa a22 4500 605468621 CHVBK 20210128100317.0 cr unu---uuuuu 210128e20150601xx s 000 0 eng 10.1007/s00500-014-1551-y doi (NATIONALLICENCE)springer-10.1007/s00500-014-1551-y The time complexity analysis of a class of gene expression programming [Elektronische Daten] [Xin Du, Youcong Ni, Datong Xie, Xin Yao, Peng Ye, Ruliang Xiao] 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. Springer-Verlag Berlin Heidelberg, 2014 GEP nationallicence ME-GEP nationallicence Average time complexity nationallicence Artificial fitness level nationallicence Markov chain nationallicence Du Xin Faculty of Software, Fujian Normal University, 350108, Fujian, China aut Ni Youcong Faculty of Software, Fujian Normal University, 350108, Fujian, China aut Xie Datong Department of Information Management Engineering, Fujian Commercial College, 350012, Fujian, China aut Yao Xin The Centre of Excellence for Research in Computational Intelligence and Applications(CERCIA), School of Computer Science, The University of Birmingham, Edgbaston, B15 2TT, Birmingham, UK aut Ye Peng College of Mathematics and Computer, Wuhan Textile University, 430073, Wuhan, China aut Xiao Ruliang Faculty of Software, Fujian Normal University, 350108, Fujian, China aut Soft Computing Springer Berlin Heidelberg 19/6(2015-06-01), 1611-1625 1432-7643 19:6<1611 2015 19 500 https://doi.org/10.1007/s00500-014-1551-y text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1551-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Du Xin Faculty of Software, Fujian Normal University, 350108, Fujian, China aut NATIONALLICENCE 700 1- Ni Youcong Faculty of Software, Fujian Normal University, 350108, Fujian, China aut NATIONALLICENCE 700 1- Xie Datong Department of Information Management Engineering, Fujian Commercial College, 350012, Fujian, China aut NATIONALLICENCE 700 1- Yao Xin The Centre of Excellence for Research in Computational Intelligence and Applications(CERCIA), School of Computer Science, The University of Birmingham, Edgbaston, B15 2TT, Birmingham, UK aut NATIONALLICENCE 700 1- Ye Peng College of Mathematics and Computer, Wuhan Textile University, 430073, Wuhan, China aut NATIONALLICENCE 700 1- Xiao Ruliang Faculty of Software, Fujian Normal University, 350108, Fujian, China aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/6(2015-06-01), 1611-1625 1432-7643 19:6<1611 2015 19 500