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Modelling Hierarchical Genetic Strategy as a Family of Markov Chains

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Parallel Processing and Applied Mathematics (PPAM 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2328))

Abstract

We present Hierarchical Genetic Strategy (HGS) as a family of Markov chains applying Vose’s mathematical model for Simple Genetic Algorithm. Studying its asymptotic properties and performing simply experiments we try to compare efficiency of HGS and sequential genetic algorithms.

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References

  1. Kołodziej J., Gwizdała R., Wojtusiak J.: Hierarchical Genetic Strategy as a Method of Improving Search Efficiency, Advances in Multi-Agents Systems, R Schaefer and S. Sędziwy eds.,UJ Press, Chapter 7, 200, pp. 149–161.

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  2. Obuchowicz A.: Performance Analysis of Crossing Multi-Dimensional Saddles by Evolutionary Search Algorithms, Proc. of the 4-th Polish Conference on Evolutionary Algorithms, Lcadek Zdrój, 2000.06.05-08, pp. 155–162.

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  3. Schaefer R., Jabłoński Z. J.: On The Convergence Of Sampling Measures In Global Genetic Search, Submitted to Proc. of the PPAM’01, 4-th International Conference On Parallel Processing And Applied Mathematics, Nałęczów, Poland, 2001,09.9-12.

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  4. Vose M.D.: The Simple Genetic Algorithm, MIT Press (1999).

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© 2002 Springer-Verlag Berlin Heidelberg

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Kolodziej, J. (2002). Modelling Hierarchical Genetic Strategy as a Family of Markov Chains. In: Wyrzykowski, R., Dongarra, J., Paprzycki, M., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2001. Lecture Notes in Computer Science, vol 2328. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48086-2_65

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  • DOI: https://doi.org/10.1007/3-540-48086-2_65

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43792-5

  • Online ISBN: 978-3-540-48086-0

  • eBook Packages: Springer Book Archive

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