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Convergence analysis of gene expression programming based on maintaining elitist

Published: 12 June 2009 Publication History

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

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Ferreira C. Gene Expression Programming: A New Adaptive Algorithm for Solving Problems. Complex Systems, 13(2):87--129,2001.
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A.E. Eiben and E.H.L. Aarts and K.M. Van Hee. Global convergence of genetic algorithms: A Markov chain analysis. Parallel Problem Solving from Nature, Springer Berlin/Heidelberg, 1991.
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D.B. Fogel. Evolving artificial intelligence. Doctoral Thesis, University of California, San Diego,1992
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J. S. Rosenthal. Minorization Conditions and Convergence Rates for Markov Chain Monte Carlo. Journal of the American Statistical Association,90(430):558--566,1995.
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J.S. Rosenthal.Quantitive Convergence Rates of Markov Chains: A Simple Account. Electronic Communications in Probability, pages 123--128,7 2002.
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Yuan Chang-an etc. Function Mining Based on Gene Expression Programming Convergence Analysis and Remnant-guided Evolution Algorithm. Journal of Sichun University, 36(6):100--105, 2004.
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M. Iosifescu. Finite Markov Processes and Their Application. Wiley, Chichester, 1980.
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Renjie Shi. Markov chain and its application. XiDian University Press,Xi'an,1992.

Cited By

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  • (2017)Gene Expression Programming: A Survey [Review Article]IEEE Computational Intelligence Magazine10.1109/MCI.2017.270861812:3(54-72)Online publication date: 1-Aug-2017
  • (2017)Integration of Reaction Kinetics Theory and Gene Expression Programming to Infer Reaction MechanismApplications of Evolutionary Computation10.1007/978-3-319-55849-3_4(53-66)Online publication date: 25-Mar-2017
  • (2015)The time complexity analysis of a class of gene expression programmingSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-014-1551-y19:6(1611-1625)Online publication date: 1-Jun-2015

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    cover image ACM Conferences
    GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
    June 2009
    1112 pages
    ISBN:9781605583266
    DOI:10.1145/1543834

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 12 June 2009

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    Author Tags

    1. GEP
    2. convergence
    3. convergence speed
    4. markov chain

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    View all
    • (2017)Gene Expression Programming: A Survey [Review Article]IEEE Computational Intelligence Magazine10.1109/MCI.2017.270861812:3(54-72)Online publication date: 1-Aug-2017
    • (2017)Integration of Reaction Kinetics Theory and Gene Expression Programming to Infer Reaction MechanismApplications of Evolutionary Computation10.1007/978-3-319-55849-3_4(53-66)Online publication date: 25-Mar-2017
    • (2015)The time complexity analysis of a class of gene expression programmingSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-014-1551-y19:6(1611-1625)Online publication date: 1-Jun-2015

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