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A Model Based on Biological Invasions for Island Evolutionary Algorithms

  • Ivanoe De Falco
  • Antonio Della Cioppa
  • Domenico Maisto
  • Umberto Scafuri
  • Ernesto Tarantino
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7401)

Abstract

Migration strategy plays an important role in designing effective distributed evolutionary algorithms. Here, a novel migration model inspired to the phenomenon known as biological invasion is adopted. The migration strategy is implemented through a multistage process involving large invading subpopulations and their competition with native individuals. In this work such a general approach is used within an island parallel model adopting Differential Evolution as the local algorithm. The resulting distributed algorithm is evaluated on a set of well known test functions and its effectiveness is compared against that of a classical distributed Differential Evolution.

Keywords

massive migration biological invasion distributed EAs 

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References

  1. 1.
    Holland, J.: Adaptation in natural and artificial systems (1975)Google Scholar
  2. 2.
    Cantú-Paz, E.: A summary of research on parallel genetic algorithms. Technical Report 95007, University of Illinois, Urbana–Champaign, USA (1995)Google Scholar
  3. 3.
    Mühlenbein, H.: Evolution in time and space - the parallel genetic algorithm. In: Rawlins, G. (ed.) Foundation of Genetic Algorithms, pp. 316–337. Morgan Kaufmann, San Mateo (1992)Google Scholar
  4. 4.
    Grajdeanu, A.: Parallel models for evolutionary algorithms. Technical report, ECLab, Summer Lecture Series, George Mason University, 38 (2003)Google Scholar
  5. 5.
    Skolicki, K., De Jong, K.: The influence of migration sizes and intervals on island models. In: Proc. of the Conference of Genetic and Evolutionary Computation, pp. 1295–1302. Association for Computing Machinery, Inc. (ACM) (2005)Google Scholar
  6. 6.
    Tomassini, M.: Spatially structured evolutionary algorithms. Springer (2005)Google Scholar
  7. 7.
    Alba, E., Tomassini, M.: Parallelism and evolutionary algorithms. IEEE Trans. on Evolutionary Computation 6(5), 443–462 (2002)CrossRefGoogle Scholar
  8. 8.
    Shigesada, N., Kawasaki, K.: Biological invasions: theory and practice. Oxford University Press, USA (1997)Google Scholar
  9. 9.
    Suarez, A., Tsutsui, N.: The evolutionary consequences of biological invasions. Molecular Ecology 17(1), 351–360 (2008)CrossRefGoogle Scholar
  10. 10.
    Strayer, D., Eviner, V., Jeschke, J., Pace, M.: Understanding the long-term effects of species invasions. Trends in Ecology & Evolution 21(11), 645–651 (2006)CrossRefGoogle Scholar
  11. 11.
    Catford, J., Jansson, R., Nilsson, C.: Reducing redundancy in invasion ecology by integrating hypotheses into a single theoretical framework. Diversity and Distributions 15(1), 22–40 (2009)CrossRefGoogle Scholar
  12. 12.
    Kolar, C., Lodge, D.: Progress in invasion biology: predicting invaders. Trends in Ecology & Evolution 16(4), 199–204 (2001)CrossRefGoogle Scholar
  13. 13.
    Simberloff, D.: The role of propagule pressure in biological invasions. Annual Review of Ecology, Evolution, and Systematics 40, 81–102 (2009)CrossRefGoogle Scholar
  14. 14.
    Lockwood, J., Cassey, P., Blackburn, T.: The role of propagule pressure in explaining species invasions. Trends in Ecology & Evolution 20(5), 223–228 (2005)CrossRefGoogle Scholar
  15. 15.
    Price, K., Storn, R.: Differential evolution. Dr. Dobb’s Journal 22(4), 18–24 (1997)MathSciNetGoogle Scholar
  16. 16.
    Storn, R., Price, K.: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11(4), 341–359 (1997)MathSciNetzbMATHCrossRefGoogle Scholar
  17. 17.
    De Falco, I., Della Cioppa, A., Maisto, D., Scafuri, U., Tarantino, E.: Satellite Image Registration by Distributed Differential Evolution. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 251–260. Springer, Heidelberg (2007)Google Scholar
  18. 18.
    Cantú-Paz, E.: Migration policies, selection pressure, and parallel evolutionary algorithms. Journal of Heuristics 7(4), 311–334 (2001)zbMATHCrossRefGoogle Scholar
  19. 19.
    Zaharie, D.: Parameter adaptation in differential evolution by controlling the population diversity. In: Petcu, D., et al. (eds.) Proc. of the International Workshop on Symbolic and Numeric Algorithms for Scientific Computing, pp. 385–397 (2002)Google Scholar
  20. 20.
    Tasoulis, D., Pavlidis, N., Plagianakos, V., Vrahatis, M.: Parallel differential evolution. In: Proc. of the Congress on Evolutionary Computation, June 19-23, vol. 2, pp. 2023–2029 (2004)Google Scholar
  21. 21.
    Kozlov, K.N., Samsonov, A.M.: New migration scheme for parallel differential evolution. In: Proc. of the International Conference on Bioinformatics of Genome Regulation and Structure, pp. 141–144 (2006)Google Scholar
  22. 22.
    Apolloni, J., Leguizamón, G., García-Nieto, J., Alba, E.: Island based distributed differential evolution: an experimental study on hybrid testbeds. In: Proc. of the Eight International Conference on Hybrid Intelligent Systems, pp. 696–701. IEEE Press (2008)Google Scholar
  23. 23.
    Weber, M., Neri, F., Tirronen, V.: Distributed differential evolution with explorative-exploitative population families. Genetic Programming and Evolvable Machines 10(4), 343–371 (2009)CrossRefGoogle Scholar
  24. 24.
    Weber, M., Neri, F., Tirronen, V.: Scale factor inheritance mechanism in distributed differential evolution. Soft Computing 14, 1187–1207 (2010)CrossRefGoogle Scholar
  25. 25.
    Ishimizu, T., Tagawa, K.: A structured differential evolution for various network topologies. Int. Journal of Computers and Communications 4(1), 2–8 (2010)Google Scholar
  26. 26.
    Baker, J.: Reducing bias and inefficiency in the selection algorithm. In: Proc. of the Second International Conference on Genetic Algorithms and their Application, pp. 14–21. L. Erlbaum Associates Inc. (1987)Google Scholar
  27. 27.
    Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics Bulletin 1(6), 80–83 (1945)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ivanoe De Falco
    • 1
  • Antonio Della Cioppa
    • 2
  • Domenico Maisto
    • 1
  • Umberto Scafuri
    • 1
  • Ernesto Tarantino
    • 1
  1. 1.ICAR–CNRNaplesItaly
  2. 2.DIEIIUniversity of SalernoFiscianoItaly

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