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On the Benefits of Aging and the Importance of Details

  • Thomas Jansen
  • Christine Zarges
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6209)

Abstract

Aging is a concept that is used in many artificial immune system implementations. It is an important tool that helps to cope with multi-modal problems by increasing diversity and allowing to restart the search in different parts of the search space. The current theoretical understanding of the details of aging is still very limited. This holds with respect to parameter settings, the relationship of different variants, the specific mechanisms that make aging useful, and implementation details. While implementation details seem to be the least important part they can have a surprisingly huge impact. This is proven by means of theoretical analysis for a carefully constructed example problem as well as thorough experimental investigations of aging for this problem.

Keywords

Global Optimum Local Optimum Crossover Probability Search Point Theoretical Bound 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Castrogiovanni, M., Nicosia, G., Rascunà, R.: Experimental analysis of the aging operator for static and dynamic optimisation problems. In: Apolloni, B., Howlett, R.J., Jain, L. (eds.) KES 2007, Part III. LNCS (LNAI), vol. 4694, pp. 804–811. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Cutello, V., Morelli, G., Nicosia, G., Pavone, M.: Immune algorithms with aging operators for the string folding problem and the protein folding problem. In: Raidl, G.R., Gottlieb, J. (eds.) EvoCOP 2005. LNCS, vol. 3448, pp. 80–90. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  3. 3.
    Cutello, V., Nicosia, G., Pavone, M.: Exploring the capability of immune algorithms: A characterization of hypermutation operators. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds.) ICARIS 2004. LNCS, vol. 3239, pp. 263–276. Springer, Heidelberg (2004)Google Scholar
  4. 4.
    Dasgupta, D., Niño, L.F.: Immunological Computation: Theory and Applications, Auerbach (2008)Google Scholar
  5. 5.
    de Castro, L., Zuben, F.: Learning and optimization using the clonal selection principle. IEEE Trans. on Evol. Comp. 6(3), 239–251 (2002)CrossRefGoogle Scholar
  6. 6.
    de Castrop, L., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach. Springer, Heidelberg (2002)Google Scholar
  7. 7.
    Harik, G., Cantú-Paz, E., Goldberg, D., Miller, B.: The gambler’s ruin problem, genetic algorithms, and the sizing of populations. Evol. Comp. 7(3), 231–253 (1999)CrossRefGoogle Scholar
  8. 8.
    Horoba, C., Jansen, T., Zarges, C.: Maximal age in randomized search heuristics with aging. In: Proc. of GECCO, pp. 803–810. ACM Press, New York (2009)CrossRefGoogle Scholar
  9. 9.
    Jansen, T., Zarges, C.: Comparing different aging operators. In: Proc. of the 8th ICARIS, pp. 95–108. Springer, Heidelberg (2009)Google Scholar
  10. 10.
    Jansen, T., Zarges, C.: Aging beyond restarts. In: Proc. of GECCO. ACM, New York (to appear, 2010)Google Scholar
  11. 11.
    Jong, K.A.D.: Evolutionary Computation. A Unified Approach. MIT Press, Cambridge (2006)zbMATHGoogle Scholar
  12. 12.
    Kelsey, J., Timmis, J.: Immune inspired somatic contiguous hypermutation for function optimisation. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 207–218. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  13. 13.
    Witt, C.: Runtime analysis of the (μ+1) EA on simple pseudo-Boolean functions. Evol. Comp. 14(1), 65–86 (2006)MathSciNetGoogle Scholar
  14. 14.
    Zarges, C.: Rigorous runtime analysis of inversely fitness proportional mutation rates. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 112–122. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  15. 15.
    Zarges, C.: On the utility of the population size for inversely fitness proportional mutation rates. In: Proc. of the 10th FOGA, pp. 39–46. ACM Press, New York (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Thomas Jansen
    • 1
  • Christine Zarges
    • 2
  1. 1.Department of Computer ScienceUniversity College CorkCorkIreland
  2. 2.Fakultät für Informatik, LS 2TU DortmundDortmundGermany

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