Immune Inspired Somatic Contiguous Hypermutation for Function Optimisation

  • Johnny Kelsey
  • Jon Timmis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2723)

Abstract

When considering function optimisation, there is a trade off between quality of solutions and the number of evaluations it takes to find that solution. Hybrid genetic algorithms have been widely used for function optimisation and have been shown to perform extremely well on these tasks. This paper presents a novel algorithm inspired by the mammalian immune system, combined with a unique mutation mechanism. Results are presented for the optimisation of twelve functions, ranging in dimensionality from one to twenty. Results show that the immune inspired algorithm performs significantly fewer evaluations when compared to a hybrid genetic algorithm, whilst not sacrificing quality of the solution obtained.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Andre, J., Siarry, P. and Dognon, T. An improvement of the standard genetic algorithm fighting premature convergence in continuous optimisation. Advances in Engineering Software. 32. p. 49–60, 2001.CrossRefGoogle Scholar
  2. 2.
    Berger, J., Sassi, J and Salois, M. A Hybrid Genetic Algorithm for the Vehicle Routing Problem with Time Windows and Itinerary Constraints, Proceedings of the Genetic and Evolutionary Computation Conference, 1999, 1, 44–51, Orlando, Florida, USA, Morgan Kaufmann. 1-55860-611-4Google Scholar
  3. 3.
    Burke E.K., Elliman D.G. and Weare R.F., A hybrid genetic algorithm for highly constrained timetabling problems, 6th International Conference on Genetic Algorithms (ICGA’95, Pittsburgh, USA, 15th–19th July 1995), Morgan Kaufmann, San Francisco, CA, USA, pages 605–610, 1995Google Scholar
  4. 4.
    de Castro L. Von Zuben F. Clonal selection principle for learning and optimisation. IEEE Transactions on Evolutionary Computation. 2002.Google Scholar
  5. 5.
    de Castro L and Timmis J. Artificial immune systems: a new computational intelligence approach Springer-Verlag. ISBN 1-85233-594-7. 2002Google Scholar
  6. 6.
    de Castro L and Timmis J. An artificial immune network for multimodal optimisation In 2002 Congress on Evolutionary Computation. Part of the 2002 IEEE World Congress on Computational Intelligence, pages 699–704, Honolulu, Hawaii, USA, May 2002. IEEE.Google Scholar
  7. 7.
    Eiben, A and van Kemenade, C. Performance of multi-parent crossover operators on numerical function optimization problems Technical Report TR-9533, Leiden University, 1995.Google Scholar
  8. 8.
    Farmer, J.D., Packard, N.H., and Perelson, A. The Immune System, Adaptation and Machine Learning. Physica, 1986. 22(D): p. 187–204MathSciNetGoogle Scholar
  9. 9.
    Forrest S., Hofmeyr S. and Somayaji S. Computer Immunology. Communications of the ACM. 40(10). pages 88–96. 1997CrossRefGoogle Scholar
  10. 10.
    Goldberg, D. and Voessner, S. Optimizing global-local search hybrids, Proceedings of the Genetic and Evolutionary Computation Conference, 1, 13–17, Morgan Kaufmann, Orlando, Florida, USA, 1-55860-611-4, 220–228, 1999.Google Scholar
  11. 11.
    Hajela, P. and Yoo, J. Immune network modelling in design optimisation. In New Ideas in Optimisation. D. Corne, M. Dorigo and F. Glover (eds), McGraw-Hill. pp. 203–215, 1999.Google Scholar
  12. 12.
    Hart, E. and Ross, P. The evolution and analysis of a potential antibody library for use in job-shop scheduling. In New Ideas in Optimisation. Corne, D., Dorigo, M. and Glover, F. (eds), p. 185–202, 1999.Google Scholar
  13. 13.
    Jerne, N.K. Towards a network theory of the immune system. Annals of Immunology, 1974. 125C: p. 373–389.Google Scholar
  14. 14.
    Kephart, J. A biologically inspired immune system for computers. Artificial Life IV. 4th International Workshop on the Synthesis and Simulation of Living Systems. MIT Press, 1994.Google Scholar
  15. 15.
    Lamlum, H., et. al. The type of somatic mutation at APC in familial adenomatous polyposis is determined by the site of the germline mutation: a new facet to Knudson’s ‘two-hit’ hypothesis. Nature Medicine, 1999, 5: pages 1071–1075.CrossRefGoogle Scholar
  16. 16.
    Nguyen, H. Yoshihara, I., Yamamori, M. and Yasunaga, M. A parallel hybrid genetic algorithm for multiple protein sequence alignment, Proceedings of the 2002 Congress on Evolutionary Computation CEC2002, 309–314, 2002, IEEE Press.Google Scholar
  17. 17.
    Rosin-Arbesfeld, R., Townsley, F. and Bienz, M. The APC tumour suppressor has a nuclear export function. Letters to nature, 2000, 406: pages 1009–1012.CrossRefGoogle Scholar
  18. 18.
    Timmis, J. and Neal, M. A resource limited artificial immune system for data analysis. Knowledge Based Systems. 14(3–4): p. 121–130, 2001.CrossRefGoogle Scholar
  19. 19.
    Coello, C. Coello and Cruz Cortes, N. An approach to solve multiobjective optimization problems based on an artificial immune system, Proceedings of the 1st International Conference on Artificial Immune Systems (ICARIS) 1, 212–221, 2002Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Johnny Kelsey
    • 1
  • Jon Timmis
    • 1
  1. 1.Computing LaboratoryUniversity of KentCanterbury, KentUK

Personalised recommendations