Convergence analysis of gene expression programming based on maintaining elitist

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Convergence analysis of gene expression programming based on maintaining elitist

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ACM/SIGEVO Summit on Genetic and Evolutionary Computation archive
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation table of contents

Shanghai, China

POSTER SESSION: Poster sessions table of contents

Pages 823-826

Year of Publication: 2009

ISBN:978-1-60558-326-6

Authors

Xin Du	State-key Lab of software engineering,Wuhan University/Department of Information and engineering, Shijiazhuang University of Eco, Wuhan, China
Lin Xin Ding	State-key Lab of software engineering,Wuhan University, Wuhan, China
Chen Wang Xie	State-key Lab of software engineering,Wuhan University, Wuhan, China
Xing Xu	State-key Lab of software engineering,Wuhan University, Wuhan, China
Shen wen Wang	Shijiazhuang University of Economics, Shijiazhuang, China
Li Chen	State-key Lab of software engineering,Wuhan University, Wuhan, China

Sponsors

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery

Publisher

ACM New York, NY, USA

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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.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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