Advertisement

Genetic Programming and Evolvable Machines

, Volume 11, Issue 2, pp 205–225 | Cite as

Variable population size and evolution acceleration: a case study with a parallel evolutionary algorithm

  • Ting HuEmail author
  • Simon Harding
  • Wolfgang Banzhaf
Contributed Article

Abstract

With current developments of parallel and distributed computing, evolutionary algorithms have benefited considerably from parallelization techniques. Besides improved computation efficiency, parallelization may bring about innovation to many aspects of evolutionary algorithms. In this article, we focus on the effect of variable population size on accelerating evolution in the context of a parallel evolutionary algorithm. In nature it is observed that dramatic variations of population size have considerable impact on evolution. Interestingly, the property of variable population size here arises implicitly and naturally from the algorithm rather than through intentional design. To investigate the effect of variable population size in such a parallel algorithm, evolution dynamics, including fitness progression and population diversity variation, are analyzed. Further, this parallel algorithm is compared to a conventional fixed-population-size genetic algorithm. We observe that the dramatic changes in population size allow evolution to accelerate.

Keywords

Variable population size Population bottleneck Evolution acceleration Parallel computing GPU 

Notes

Acknowledgments

W.B. acknowledges NSERC support under Discovery Grant RGPIN 283304-07.

References

  1. 1.
    E. Alba, M. Tomassini, Parallelism and evolutionary algorithms. IEEE Trans. Evol. Comput. 6(5), 443–462 (2002)CrossRefGoogle Scholar
  2. 2.
    S.H. Ambrose, Late pleistocene human population bottlenecks, volcanicwinter, and the differentiation of modern humans. J. Human Evol. 34(6), 623–651 (1998)CrossRefGoogle Scholar
  3. 3.
    J. Arabas, Z. Michalewicz, J. Mulawka, GAVaPS—a genetic algorithm with varying population size, in Proceedings of IEEE Congress on Evolutionary Computation (CEC 1994) IEEE Press, 1994, pp. 73–78Google Scholar
  4. 4.
    T. Back, A.E. Eiben, N.A.L. van der Vaart, An empirical study on GAs “without parameters”, in Proceedings of the 6th International Conference on Parallel Problem Solving from Nature (PPSN VI), vol. 1917 of LNCS (Springer, 2000), pp. 315–324Google Scholar
  5. 5.
    W. Banzhaf, S. Harding, W.B. Langdon, G. Wilson, in Accelerating Genetic Programming Through Graphics Processing Units, ed. by, R.L. Riolo, T. Soule, B. Worzel. Genetic Programming Theory and Practice VI , Genetic and Evolutionary Computation, chapter 15 (Springer, Berlin, 2008), pp. 229–249Google Scholar
  6. 6.
    G. Cochran, H. Harpending, The 10,000 Year Explosion: How Civilization Accelerated Human Evolution (Basic Books, New York, NY, USA, 2009)Google Scholar
  7. 7.
    A.E. Eiben, E. Marchiori, V.A. Valkó, Evolutionary algorithms with on-the-fly population size adjustment, in Proceedings of the 8th International Conference on Parallel Problem Solving from Nature (PPSN VIII), vol. 3242 of LNCS (Springer, 2004), pp. 41–50Google Scholar
  8. 8.
    C. Fernandes, A. Rosa, Self-regulated population size in evolutionary algorithms, in Proceedings of the 9th International Conference on Parallel Problem Solving from Nature (PPSN IX), vol. 4193 of LNCS (Springer, 2006), pp. 920–929Google Scholar
  9. 9.
    R. A. Fisher, Genetical Theory of Natural Selection (Clarendon, Oxford, 1930)zbMATHGoogle Scholar
  10. 10.
    R. Frankham, Relationship of genetic variation to population size in wildlife. Conserv. Biol. 10(6), 1500–1508 (1996)CrossRefGoogle Scholar
  11. 11.
    A. Gherman, P.E. Chen, T.M. Teslovich, P. Stankiewicz, M. Withers, C.S. Kashuk, A. Chakravarti, J.R. Lupski, D.J. Cutler, N. Katsanis, Population bottlenecks as a potential major shaping force of human genome architecture. PloS Genet. 3(3), 1223–1231 (2007)Google Scholar
  12. 12.
    D.E. Goldberg, Sizing populations for serial and parallel genetic algorithms, in Proceedings of the Third International Conference on Genetic algorithms, (San Francisco, CA, Morgan Kaufmann Publishers Inc., 1989), pp. 70–79Google Scholar
  13. 13.
    D.E. Goldberg, K. Deb, J.H. Clark, Genetic algorithms, noise, and the sizing of populations. Complex Syst. 6(4), 333–362 (1992)zbMATHGoogle Scholar
  14. 14.
    G.R. Harik, F.G. Lobo, A parameter-less genetic algorithm, in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 1999) (Morgan Kaufmann, 1999), pp. 258–267Google Scholar
  15. 15.
    D.L. Hartl, A.G. Clark, Principles of Population Genetics, 4th edn. (Sinauer Associates Inc. Publisher, Sunderland, MA, 2007)Google Scholar
  16. 16.
    J. Hawks, K. Hunley, S.-H. Lee, M. Wolpoff, Population bottlenecks and pleistocene human evolution. Mole. Biol. Evol. 17(1), 2–22 (2000)Google Scholar
  17. 17.
    J. Hawks, E.T. Wang, G.M. Cochran, H.C. Harpending, R.K. Moyzis, Recent acceleration of human adaptive evolution. Proc. Nat. Acad. Sci. 104(52), 20753–20758 (2007)CrossRefGoogle Scholar
  18. 18.
    T. Hu, W. Banzhaf, The role of population size in rate of evolution in genetic programming, in Proceedings of the 12th European Conference on Genetic Programming (EuroGP 2009), vol. 5481 of LNCS (Springer, 2009), pp. 85–96Google Scholar
  19. 19.
    J. Kennedy, W. Spears, Matching algorithms to problems: an experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator, in Proceedings of the IEEE International Conference on Evolutionary Computation (IEEE, 1998), pp. 74–77Google Scholar
  20. 20.
    V.K. Koumousis, C.P. Katsaras, A saw-tooth genetic algorithm combining the effects of variable population size and reinitialization to enhance performance. IEEE Trans. Evol. Comput. 10(1), 19–28 (2006)CrossRefGoogle Scholar
  21. 21.
    F.G. Lobo, C.F. Lima, A review of adaptive population sizing schemes in genetic algorithms, in Proceedings of the 2005 Workshops on Genetic and Evolutionary Computation (GECCO 2005) (ACM, 2005), pp. 228–234Google Scholar
  22. 22.
    R.W. Morrison, K.A.D. Jong, Measurement of population diversity, in Proceedings of the 3rd International Conference on Evolutionary Algorithm, vol. 2310 of LNCS (Springer, 2001), pp. 31–41Google Scholar
  23. 23.
    M. Nei, T. Maruyama, R. Chakraborty, The bottleneck effect and genetic variability in populations. Evolution 29(1), 1–10 (1975)CrossRefGoogle Scholar
  24. 24.
    T. Ohta, Population size and rate of evolution. J. Mole. Evol. 1(4), 305–314 (1972)CrossRefGoogle Scholar
  25. 25.
    D. Schlierkamp-Voosen, H. Muhlenbein, Strategy adaptation by competing subpopulations, in Proceedings of the 3rd International Conference on Parallel Problem Solving from Nature (PPSN III), vol. 866 of LNCS (Springer, 1994), pp. 199–208Google Scholar
  26. 26.
    R.E. Smith, Adaptively resizing populations: an algorithm and analysis, in Proceedings of the 5th International Conference on Genetic Algorithms (San Francisco, CA, USA Morgan Kaufmann Publishers Inc, 1993) , 653 pp.Google Scholar
  27. 27.
    M.E. Soule, B.A. Wilcox, Conservation Biology. An evolutionary-ecological perspective. (Sinauer Associates, Sunderland, MA, USA, 1980)Google Scholar
  28. 28.
    M. Tomassini, L. Vanneschi, J. Cuendet, A new technique for dynamic size populations in genetic programming, in Proceedings of IEEE Congress on Evolutionary Computation (CEC 2004) (IEEE Press, 2004), pp. 486–493Google Scholar
  29. 29.
    S. Wright, Evolution and the Genetics of Populations: Genetics and Biometric Foundations v. 4 (Variability Within and Among Natural Populations). (University of Chicago Press, Chicago, IL, 1984)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of Computer ScienceMemorial UniversitySt. John’sCanada

Personalised recommendations