Convergence analysis of gene expression programming based on maintaining elitist

Authors:
Xin Du;Lin Xin Ding;Chen Wang Xie;Xing Xu;Shen wen Wang;Li Chen
Affiliations:
State-key Lab of software engineering,Wuhan University/Department of Information and engineering, Shijiazhuang University of Eco, Wuhan, China;State-key Lab of software engineering,Wuhan University, Wuhan, China;State-key Lab of software engineering,Wuhan University, Wuhan, China;State-key Lab of software engineering,Wuhan University, Wuhan, China;Shijiazhuang University of Economics, Shijiazhuang, China;State-key Lab of software engineering,Wuhan University, Wuhan, China
Venue:
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Year:
2009

Citing 2
Cited 0

Evolving artificial intelligence

Evolving artificial intelligence
Global Convergence of Genetic Algorithms: A Markov Chain Analysis

PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper analyzes the convergence of Gene Expression Programming based on maintaining elitist(ME-GEP).It is proved that ME-GEP algorithm will converge to the global optimal solution. The convergence speed of ME-GEP algorithm is estimated by the properties of transition matrices. The result hinges on four factors: population size, minimal transposition, mutation and selection probabilities.