Skip to main content

Immune Clonal Strategies Based on Three Mutation Methods

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4222))

Abstract

Based on the clonal selection theory, the main mechanisms of clone are analyzed in this paper, a new immune operator, Clonal Operator, inspired by the Immune System is discussed firstly. Based on the Clonal operator, we propose Immune Clonal Strategy Algorithm (ICSA); three different mutation mechanisms including Gaussian mutation, Cauthy mutation and Mean mutation are used in IMSA. IMSA based on these three methods are compared with Classical Evolutionary Strategy (CES) on a set of benchmark functions, the numerical results show that ICSA is capable of avoiding prematurity, increasing the converging speed and keeping the variety of solution. Additionally, we present a general evaluation of the complexity of ICSA.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. De Castro, L.N., Von Zuben, F.J.: Artificial immune systems: Part I—basic theory and applications. Technical Report DCA-RT 01/99, School of Computing and Electrical Engineering, State University of Campinas, Brazil (1999), Available: http://www.dca.fee.unicamp.br/~Inunes/immunes.html

  2. Jiao, L.C., Liu, J., Zhong, W.C.: An organizational coevolutionary algorithm for classification. IEEE Trans. Evol. Comput. 10(1), 67–80 (2006)

    Article  Google Scholar 

  3. Liu, J., Zhong, W.C., Jiao, L.C.: A multivalent evolutionary algorithm for constraint satisfaction problems. IEEE Trans. Syst., Man, and Cybern. B. 36(1), 54–73 (2006)

    Article  Google Scholar 

  4. Forrest, S., Steven, A.H.: Immunology as Information Processing from Design Principles for Immune System & Other Distributed Autonomous Systems. In: Segel, L.A., Cohen, I.R. (eds.), pp. 361–387. Oxford Univ. Press, Oxford (2000), www.cs.unm.edu/~forrest/publications/iaip.pdf

  5. Burnet, F.M.: The Clonal Selection Theory of Acquired Immunity. Cambridge University Press, Cambridge (1959)

    Google Scholar 

  6. Forsdyke, D.R.: The origins of the clonal selection theory of immunity as a case study for evaluation in science. The FASEB Journal 9, 164–166 (1995)

    Google Scholar 

  7. Jerne, N.K.: The natural-selection theory of antibody formation. Proceedings of the National Academy of Sciences USA 41, 849–856 (1955)

    Article  Google Scholar 

  8. Coutinho, A.: The network theory: 21 years later. Scandinavian Journal of Immunology 42, 3–8 (1995)

    Article  Google Scholar 

  9. Jerne, N.K.: Towards a network theory of the immune system. Ann. Immunology (Inst. Pasteur.) 125(C), 373–389 (1974)

    Google Scholar 

  10. DasGupta, D.: Artficial Immune Systems and Their Applications. Springer, New York (1998)

    Google Scholar 

  11. De Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach. Springer, Heidelberg (2002)

    MATH  Google Scholar 

  12. French, D.L., Reuven, L., Scharff, M.D.: The role of somatic hypermutation in the generation of antibody diversity. Science 244(4909), 1152–1157 (1989)

    Article  Google Scholar 

  13. Fogel, D.B., Atmar, J.W.: Comparing Genetic Operators with Gaussian Mutations in Simulated Evolutionary Processes Using Linear Systems. Biological Cybernetics 63, 111–114 (1993)

    Article  Google Scholar 

  14. Yao, X., Liu, Y., Lin, G.: Evolutionary Programming Made Faster. IEEE Transactions on Evolutionary Computation 3(2), 82–102 (1999)

    Article  Google Scholar 

  15. Chellapilla, K.: Combining Mutation Operators in Evolutionary Programming. IEEE Transactions Computation 2(3), 91–96 (1998)

    Article  Google Scholar 

  16. Jiao, L.C., Wang, L.: A Novel genetic Algorithm based on Immunity. IEEE Trans. On Systems, Man, and Cybernetics-Part A Systems and Humans 30(5), 551–552 (2000)

    Google Scholar 

  17. Liu, R.C., Du, H.F., Jiao, L.C.: Immunity Ployclonal Strategy. Journal of Computer Research and Development 41(4), 571–576 (2004)

    Google Scholar 

  18. Fischetti, M., Martello, S.: A Hybrid Algorithm for finding the kth Smallest of n Elements in O (n) time. Ann. Operation Res. 13, 401–419 (1998)

    MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Liu, R., Chen, L., Wang, S. (2006). Immune Clonal Strategies Based on Three Mutation Methods. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881223_15

Download citation

  • DOI: https://doi.org/10.1007/11881223_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45907-1

  • Online ISBN: 978-3-540-45909-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics