Genetic Programming and Evolvable Machines

, Volume 8, Issue 3, pp 255–286 | Cite as

A self-organizing random immigrants genetic algorithm for dynamic optimization problems

Original Paper


In this paper a genetic algorithm is proposed where the worst individual and individuals with indices close to its index are replaced in every generation by randomly generated individuals for dynamic optimization problems. In the proposed genetic algorithm, the replacement of an individual can affect other individuals in a chain reaction. The new individuals are preserved in a subpopulation which is defined by the number of individuals created in the current chain reaction. If the values of fitness are similar, as is the case with small diversity, one single replacement can affect a large number of individuals in the population. This simple approach can take the system to a self-organizing behavior, which can be useful to control the diversity level of the population and hence allows the genetic algorithm to escape from local optima once the problem changes due to the dynamics.


Genetic algorithms Self-organized criticality Dynamic optimization problems Random immigrants 


  1. 1.
    Bak, P.: How Nature Works: The Science of Self-organized Criticality. Oxford University Press (1997)Google Scholar
  2. 2.
    Bak, P., Tang, C., Wiesenfeld, K.: Self-organized criticality. An explanation of 1/f noise. Phys. Rev. Lett. 59(4), 381–384 (1987)CrossRefGoogle Scholar
  3. 3.
    Boettcher, S., Percus, A.G.: Optimization with extremal dynamics. Complexity 8(2), 57–62 (2003)CrossRefGoogle Scholar
  4. 4.
    Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer (2001)Google Scholar
  5. 5.
    Branke, J.: Evolutionary approaches to dynamic optimization problems – introduction and recent trends. In: Branke, J. (ed.) Proceedings of the GECCO Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, pp. 2–4 (2003)Google Scholar
  6. 6.
    Cedeno, W., Vemuri, V.R.: On the use of niching for dynamic landscapes. In: Proceedings of the 1997 IEEE International Conference on Evolutionary Computation, pp. 361–366 (1997)Google Scholar
  7. 7.
    Cobb, H.G.: An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuouis, time-dependent nonstationary environments. Technical Report AIC-90-001, Naval Research Laboratory, Washington, USA (1990)Google Scholar
  8. 8.
    Cobb, H.G., Grefenstette, J.J.: Genetic algorithms for tracking changing environments. In: Forrest, S. (ed.) Proceedings of the 5th International Conference on Genetic Algorithms, pp. 523–530 (1993)Google Scholar
  9. 9.
    Deb, K., Goldberg, D.E.: Analyzing deception in trap functions. In: Whitley, L.D. (ed.) Foundation of Genetic Algorithms 2, pp. 93–108 (1993)Google Scholar
  10. 10.
    Goldberg, D.A.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Publishing Company Inc. (1989)Google Scholar
  11. 11.
    Goldberg, D.A.: The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Kluwer Academic Publishers, Boston, MA (2002)MATHGoogle Scholar
  12. 12.
    Gould, S.J.: Wonderful Life: The Burgess Shale and the Nature of History. W. W. Norton and Company (1989)Google Scholar
  13. 13.
    Grefenstette, J.J.: Genetic algorithms for changing environments. In: Maenner, R., Manderick, B. (eds.) Parallel Problem Solving from Nature 2, pp. 137–144. North Holland (1992)Google Scholar
  14. 14.
    Jensen, H.J.: Self-organized Criticality: Emergent Complex Behavior in Physical and Biological Systems. Cambridge University Press (1998)Google Scholar
  15. 15.
    Jin, Y., Branke, J.: Evolutionary optimization in uncertain environments – a survey. IEEE Trans. Evol. Comput. 9(3), 303–317 (2005)CrossRefGoogle Scholar
  16. 16.
    Kauffman, S.A.: The Origins of Order: Self-organization and Selection in Evolution. Oxford University Press (1993)Google Scholar
  17. 17.
    Krink, T., Thomsen, R.: Self-organized criticality and mass extinction in evolutionary algorithms. In: Proceedings of the 2001 Congress on Evolutionary Computation, vol. 2, pp. 1155–1161 (2001)Google Scholar
  18. 18.
    Løvbjerg, M., Krink, T.: Extending particle swarm optimisers with self-organized criticality. In: Proceedings of the 2002 IEEE Congress on Evolutionary Computation, vol. 2, pp. 1588–1593 (2002)Google Scholar
  19. 19.
    Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press (1996)Google Scholar
  20. 20.
    Mori, N., Kita, H., Nishikawa, Y.: Adaptation to a changing environment by means of the feedback thermodynamical genetic algorithm. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) Parallel Problem Solving from Nature, number 1498 in LNCS, pp. 149–158. Springer (1998)Google Scholar
  21. 21.
    Raup, D.M.: Biological extinction in earth history. Science 231, 1528–1533 (1986)CrossRefGoogle Scholar
  22. 22.
    Trojanowski, K., Michalewicz, Z.: Evolutionary optimization in non-stationary environments. J. Comput. Sci. Technol. 1(2), 93–124 (2000)Google Scholar
  23. 23.
    Vavak, F., Fogarty, T.C., Jukes, K.: A genetic algorithm with variable range of local search for tracking changing environments. In: Voigt, H.-M. (ed.) Parallel Problem Solving from Nature, number 1141 in LNCS. Springer Verlag, Berlin (1996)Google Scholar
  24. 24.
    Yang, S.: Non-stationary problem optimization using the primal-dual genetic algorithm. In: Sarker, R., Reynolds, R., Abbass, H., Tan, K.-C., McKay, R., Essam, D., Gedeon, T. (eds.) Proceedings of the 2003 IEEE Congress on Evolutionary Computation, vol. 3, pp. 2246–2253 (2003)Google Scholar
  25. 25.
    Yang, S.: Constructing dynamic test environments for genetic algorithms based on problem difficulty. In: Proceedings of the 2004 IEEE Congress on Evolutionary Computation, vol. 2, pp. 1262–1269 (2004)Google Scholar
  26. 26.
    Yang, S., Yao, X.: Experimental study on population-based incremental learning algorithms for dynamic optimization problems. Soft Comput. 9(11), 815–834 (2005)MATHCrossRefGoogle Scholar

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© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.Departamento de Física e Matemática, FFCLRPUniversidade de São Paulo (USP)Ribeirão PretoBrazil
  2. 2.Department of Computer ScienceUniversity of LeicesterLeicesterUK

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