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.
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References
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)
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)
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)
Dasgupta, D., Niño, L.F.: Immunological Computation: Theory and Applications, Auerbach (2008)
de Castro, L., Zuben, F.: Learning and optimization using the clonal selection principle. IEEE Trans. on Evol. Comp. 6(3), 239–251 (2002)
de Castrop, L., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach. Springer, Heidelberg (2002)
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)
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)
Jansen, T., Zarges, C.: Comparing different aging operators. In: Proc. of the 8th ICARIS, pp. 95–108. Springer, Heidelberg (2009)
Jansen, T., Zarges, C.: Aging beyond restarts. In: Proc. of GECCO. ACM, New York (to appear, 2010)
Jong, K.A.D.: Evolutionary Computation. A Unified Approach. MIT Press, Cambridge (2006)
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)
Witt, C.: Runtime analysis of the (μ+1) EA on simple pseudo-Boolean functions. Evol. Comp. 14(1), 65–86 (2006)
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)
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)
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Jansen, T., Zarges, C. (2010). On the Benefits of Aging and the Importance of Details. In: Hart, E., McEwan, C., Timmis, J., Hone, A. (eds) Artificial Immune Systems. ICARIS 2010. Lecture Notes in Computer Science, vol 6209. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14547-6_6
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DOI: https://doi.org/10.1007/978-3-642-14547-6_6
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