Evolutionary Intelligence

, Volume 1, Issue 2, pp 133–144 | Cite as

Evolutionary algorithms to simulate the phylogenesis of a binary artificial immune system

  • Grazziela P. Figueredo
  • Luis A. V. de Carvalho
  • Helio J. C. Barbosa
  • Nelson F. F. Ebecken
Research Paper


Four binary-encoded models describing some aspects of the phylogenetics evolution in an artificial immune system have been proposed and analyzed. The first model has focused on the evolution of a paratope’s population, considering a fixed group of epitopes, to simulate a hypermutation mechanism and observe how the system would self-adjust to cover the epitopes. In the second model, the evolution involves a group of antibodies adapting to a given antigenic molecules’ population. The third model simulated the coevolution between antibodies’ generating gene libraries and antigens. The objective was to simulate somatic recombination mechanisms to obtain final libraries apt to produce antibodies to cover any possible antigen that would appear in the pathogens’ population. In the fourth model, the coevolution involves a new population of self-molecules whose function was to establish restrictions in the evolution of libraries’ population. For all the models implemented, evolutionary algorithms (EA) were used to form adaptive niching inspired in the coevolutionary shared niching strategy ideas taken from a monopolistic competition economic model where “businessmen” locate themselves among geographically distributed “clients” so as to maximize their profit. Numerical experiments and conclusions are shown. These considerations present many similarities to biological immune systems and also some inspirations to solve real-world problems, such as pattern recognition and knowledge discovery in databases.


Artificial immune systems Evolutionary computation Artificial immune systems models 


  1. 1.
    Abbas AK, Lichman AH, Pober JS (1998) Molecular and cellular immunology (in Portuguese), 2nd edn. Revinter, Rio de JaneiroGoogle Scholar
  2. 2.
    Aguilar E (2003) Um estudo sistêmico de um modelo de sistema imune com evolução da especificidade. Master’s thesis, COPPE/UFRJGoogle Scholar
  3. 3.
    Cayzer S, Smith J, Marshall JAR, Kovacs T (2005) What have gene libraries done for artificial immune systems? In: International conference on artificial immune systemsGoogle Scholar
  4. 4.
    Cormack DH (1991) HAM histology (in Portuguese), 9 edn. Guanabara KooganGoogle Scholar
  5. 5.
    Darwin C (1872) On the origin of species by means of natural selection, or the preservation of favoured races in the struggle for life, 6th edn. John Murray, LondonGoogle Scholar
  6. 6.
    de Castro LN (2001) Immune engineering: development of computational tools inspired by the artificial immune systems (in Portuguese). Ph.D. thesis, DCA FEEC/UNICAMP, Campinas/SP, BrazilGoogle Scholar
  7. 7.
    de Castro LN, Timmis J (2002) Artificial immune systems: a new computational intelligence approach, vol 1, 1st edn. Springer, New YorkGoogle Scholar
  8. 8.
    Farmer JD, Packard NH, Perelson AS (1986) The immune system, adaptation, and machine learning. Physica 22D:187–204MathSciNetGoogle Scholar
  9. 9.
    Flores LE, Aguilar EJ, Barbosa VC, de Carvalho LAV (2004) A graph model for the evolution of specificity in humoral immunity. J Theoret Biol 229(3):311–325CrossRefMathSciNetGoogle Scholar
  10. 10.
    Forrest S, Smith RE, Javornik B, Perelson AS (1993) Using genetic algorithms to explore pattern recognition in the immune system. Evolu Comput 1(3):191–211CrossRefGoogle Scholar
  11. 11.
    Goldberg D (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, ReadingzbMATHGoogle Scholar
  12. 12.
    Goldberg DE, Wang L (1998) Adaptive niching via coevolutionary sharing. Genetic Algorithms and Evolution Strategy in Engineering and Computer Science, pp 21–38Google Scholar
  13. 13.
    Golub ES (1992) Is the function of the immune system only to protect? In: Theoretical and experimental insights into immunology, vol 66 of H: cell biology. NATO ASI Series, pp 15–26Google Scholar
  14. 14.
    Hightower R, Forrest S, Perelson AS (1995) The evolution of emergent organization in immune system gene libraries. In: Eshelman L (ed) Proceedings of the 6th international conference on genetic algorithms. Morgan Kaufmann, San Francisco, pp 344–350Google Scholar
  15. 15.
    Hillis W (1990) Co-evolving parasites improve simulated evolution as an optimization procedure. Physica D 42:228–234CrossRefGoogle Scholar
  16. 16.
    Hofmeyr SA, Forrest S (2000) Architecture for an artificial immune system. Evol Comput 8(4):443–473CrossRefGoogle Scholar
  17. 17.
    Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann ArborGoogle Scholar
  18. 18.
    Inman J (1978) The antibody combining region: speculations on the hypothesis of general multiespecificity. Theoretical Immunology, pp 243–278Google Scholar
  19. 19.
    Janeway CA, Travers P, Walport M, Shlomchik M (2001) Immunobiology: the immune system in health and disease, 5th (brazilian) edn. Artes Médicas Porto AlegreGoogle Scholar
  20. 20.
    Jerne NK (1974) Towards a network theory of the immune system. Ann Immunol (Inst. Pasteur) 125C(3):73–89Google Scholar
  21. 21.
    Kepler TB, Perelson AS (1993) Cyclic reentry of germinal center b cells and the efficiency of affinity maturation. Immunol Today 14 14:412–415CrossRefGoogle Scholar
  22. 22.
    Kepler TB, Perelson AS (1993) Somatic hypermutation in b cells: an optimal control treatment. J Theoret Biol 164:37–64CrossRefGoogle Scholar
  23. 23.
    Oprea M, Forrest S (1999) How the immune system generates diversity: pathogen space coverage with random and evolved antibody libraries. In: Banzhaf W, Daida J, Eiben AE, Garzon MH, Honavar V, Jakiela M, Smith RE (eds) Proceedings of the genetic and evolutionary computation conference, vol 2. Morgan Kaufmann, Orlando, pp 1651–1656Google Scholar
  24. 24.
    Oprea M, Kepler TB (1999) Genetic plasticity of v genes under somatic hypermutation: statistical analyses using a new resampling-based methodology. Genome Res 9(12):1294–1304CrossRefGoogle Scholar
  25. 25.
    Oprea M, Perelson A (1997) Somatic mutation leads to efficient affinity maturation when centrocytes recycle back to centroblasts. J Immunol 158:5155–5162Google Scholar
  26. 26.
    Perelson A (1989) Immune network theory. Immunol Rev 110:5–36CrossRefGoogle Scholar
  27. 27.
    Perelson AS, Weisbuch G (1997) Immunology for physicists. Rev Mod Phys 69:1219CrossRefGoogle Scholar
  28. 28.
    Ron J. The evolution of secondary organization in immune system gene librariesGoogle Scholar
  29. 29.
    Rosin C, Belew R (1995) Methods for competitive co-evolution: finding opponents worth beating. In: Eshelman L (ed) Proc of the 6th international conference on genetic algorithms and their applications. Pittsburgh, PAGoogle Scholar
  30. 30.
    Rosin C, Belew R (1997) New methods for competitive coevolution. Evol Comput 5(1):1–29CrossRefGoogle Scholar
  31. 31.
    Rumjanek VM (2001) Próprio e estranho: é essa a questão? Ciência Hoje 29:40Google Scholar
  32. 32.
    Shimura J (1996) Somatic mutations in immunoglobulin v gene determine the structure and function of the protein—an evidence from homology modeling. In: Hunter L, Klein T (eds) Biocomputing: proceedings of the 1996 Pacific symposium. World Scientific Publishing, SingaporeGoogle Scholar
  33. 33.
    Stewart J (1992) The immune system in an evolutionary perspective. In: Theoretical and experimental insights into immunology, vol 66 of H: cell biology. NATO ASI Series, pp 27–48Google Scholar
  34. 34.
    Tizard I (1985) Introduction to veterinary immunology (In Portuguese), 2nd ednGoogle Scholar
  35. 35.
    Tullock G (1967) Towards a mathematics of politics. The University of Michigan Press, Ann ArborGoogle Scholar
  36. 36.
    Twycross J, Aickelin U (2007) Biological inspiration for artificial immune systems. In: Artificial immune systemsGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Grazziela P. Figueredo
    • 1
  • Luis A. V. de Carvalho
    • 1
  • Helio J. C. Barbosa
    • 2
  • Nelson F. F. Ebecken
    • 1
  1. 1.Federal University of Rio de Janeiro - COPPERio de JaneiroBrazil
  2. 2.LNCC, MCTPetrόpolisBrazil

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