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Horizontal Gene Transfer as a Method of Increasing Variability in Genetic Algorithms

  • Wojciech RafajłowiczEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10841)

Abstract

A horizontal (or lateral) gene transfer, well known in biology is used as an additional mutation factor in genetic algorithms used for optimization. Numerical results indicate the usefulness of this concept for problems of moderate size.

References

  1. 1.
    Eiben, A.E., Bäck, T.: Empirical investigation of multi-parent recombination operators in evolution strategies. Evol. Comput. 5(3), 347–365 (1997)CrossRefGoogle Scholar
  2. 2.
    Fogel, D.B.: Evolutionary Computation: The Fossil Record. Wiley-IEEE Press, Hoboken (1998)CrossRefGoogle Scholar
  3. 3.
    Keeling, P., Palmer, J.: Horizontal gene transfer in eukaryotic evolution. Nat. Rev. Genet. 9(8), 605–618 (2008)CrossRefGoogle Scholar
  4. 4.
    Li, M., et al.: Accurate determination of geographical origin of tea based on terahertz spectroscopy. Appl. Sci. 7(2), 172 (2017)CrossRefGoogle Scholar
  5. 5.
    Kishnapillai, V.: Horizontal gene transfer. J. Genet. 75(2), 219–232 (1996)CrossRefGoogle Scholar
  6. 6.
    Ortiz-Boyer, D., Hervás-Martínez, C., García-Pedrajas, N.: CIXL2: a crossover operator for evolutionary algorithms based on population features. J. Artif. Intell. Res. (JAIR) 24, 1–48 (2005)CrossRefGoogle Scholar
  7. 7.
    Prise, K.M., et al.: A review of studies of ionizing radiation-induced double-strand break clustering. Radiat. Res. 156(5), 572–576 (2001)CrossRefGoogle Scholar
  8. 8.
    Rafajłowicz, W.: Cosmic rays inspired mutation in genetic algorithms. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2017. LNCS (LNAI), vol. 10245, pp. 418–426. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-59063-9_37CrossRefGoogle Scholar
  9. 9.
    Ramadan, B.M.S.M., et al.: Hybridization of genetic algorithm and priority list to solve economic dispatch problems. In: Region 10 Conference (TENCON). IEEE (2016)Google Scholar
  10. 10.
    Rechenberg, I.: Evolution Strategy: Optimization of Technical Systems by Means of Biological Evolution, vol. 104. Fromman-Holzboog, Stuttgart (1973)Google Scholar
  11. 11.
    Scally, A.: The mutation rate in human evolution and demographic inference. Curr. Opin. Genet. Dev. 41, 36–43 (2016)CrossRefGoogle Scholar
  12. 12.
    Schwefel, H.P.: Evolution strategy and numerical optimization. Technical University of Berlin (1975)Google Scholar
  13. 13.
    Thomas, C., Nielsen, K.: Mechanisms of, and barriers to, horizontal gene transfer between bacteria. Nat. Rev. Microbiol. 3(9), 711–721 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Engineering, Faculty of ElectronicsWrocław University of Science and TechnologyWrocławPoland

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