Skip to main content
SpringerLink
Log in

Improving Artificial Immune System Performance: Inductive Bias and Alternative Mutations

  • Conference paper
Artificial Immune Systems (ICARIS 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5132))

Included in the following conference series:

Abstract

Research works related to the Artificial Immune System (AIS) and their applications have been extensively reported during the last decade. In this work, we proposed an inductive bias heuristic called neighbourhood improvement within the classical AIS for improving its performance. We also demonstrated alternative mutation mechanisms for cloning the elite antibodies. Computational experiments using the proposed heuristic and mechanisms to find the near optimal solutions of travelling salesman problems were conducted. The results obtained from the modified AIS were compared with those obtained from other metaheuristics. It was found that the performance of the modified AIS adopting the proposed heuristic and mechanisms outperformed the conventional AIS and other metaheuristics.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing surveys 35, 268–308 (2003)

    Article  Google Scholar 

  2. Nagar, A., Haddock, J., Heragu, S.: Multiple and bicriteria scheduling: A literature survey. European Journal of Operational Research 81, 88–104 (1995)

    Article  MATH  Google Scholar 

  3. Chen, K., Ji, P.: A mixed integer programming model for advanced planning and scheduling (APS). European Journal of Operational Research 181, 515–522 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  4. Brucker, P., Knust, S., Schoo, A., Thiele, O.: A branch and bound algorithm for the resource-constrained project scheduling problem. European Journal of Operational Research 107, 272–288 (1998)

    Article  MATH  Google Scholar 

  5. Choi, J., Realff, M.J., Lee, J.H.: Dynamic programming in a heuristically confined state space: a stochastic resource-constrained project scheduling application. Computers & Chemical Engineering 28, 1039–1058 (2004)

    Article  Google Scholar 

  6. Engin, O., Doyen, A.: Artificial immune systems and applications in industrial problems. G. U. Journal of Science. 17, 71–84 (2004)

    Google Scholar 

  7. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimisation by simulated annealing. Science 220, 671–679 (1983)

    Article  MathSciNet  Google Scholar 

  8. Glover, F.: Tabu search - part I. ORSA Journal on Computing 1, 190–206 (1986)

    Google Scholar 

  9. Haykin, S.: Neural networks: A comprehensive foundation, 2nd edn. Prentice-Hall, Englewood Cliffs (1999)

    MATH  Google Scholar 

  10. Goldberg, D.E.: Genetic Algorithms in Search, Optimisation and Machine Learning. Addison-Wesley, Massachusetts (1989)

    Google Scholar 

  11. Eusuff, M., Lansey, K., Pasha, F.: Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Engineering Optimization 38, 129–154 (2006)

    Article  MathSciNet  Google Scholar 

  12. Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann Publishers, San Francisco (2001)

    Google Scholar 

  13. Dorigo, M., Stutzle, T.: Ant Colony Optimization. Bradford Book, Massachusetts (2004)

    MATH  Google Scholar 

  14. Hart, E.A., Timmis, J.: Application areas of AIS: The past, the present and the future. Applied Soft Computing 8, 191–201 (2008)

    Article  Google Scholar 

  15. Aytug, H., Knouja, M., Vergara, F.E.: Use of genetic algorithms to solve production and operations management problems: a review. International Journal of Production Research 41, 3955–4009 (2003)

    Article  Google Scholar 

  16. Chaudhry, S.S., Luo, W.: Application of genetic algorithms in production and operations management: a review. International Journal of Production Research 43, 4083–4101 (2005)

    Article  MATH  Google Scholar 

  17. Dorigo, M., Blum, C.: Ant colony optimization theory: A survey. Theoretical Computer Science 344, 243–278 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  18. Dasgupta, D.: Artificial Immune Systems and Their Applications. Springer, Heidelberg (1998)

    Google Scholar 

  19. De Castro, L.: Artificial Immune Systems: Theory and Applications. In: Brazilian Symposium on Neural Networks, Rio de Janeiro, Brazil (2000)

    Google Scholar 

  20. Timmis, J.: Artificial Immune Systems - today and tomorrow. Natural Computing 6, 1–18 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  21. Freitas, A., Timmis, J.: Revisiting the Foundations of Artificial Immune Systems: A Problem-Oriented Perspective. In: Timmis, J., Bentley, P.J., Hart, E. (eds.) ICARIS 2003. LNCS, vol. 2787, pp. 229–241. Springer, Heidelberg (2003)

    Google Scholar 

  22. Chandrasekaran, M., Asokan, P., Kumanan, S., Balamurugan, T., Nickolas, S.: Solving job shop scheduling problems using artificial immune system. International Journal of Advanced Manufacturing Technology 31, 580–593 (2006)

    Article  Google Scholar 

  23. Engin, O., Doyen, A.: A new approach to solve hybrid flow shop scheduling problems by artificial immune system. Future Generation Computer Systems 20, 1083–1095 (2004)

    Article  Google Scholar 

  24. Pongcharoen, P., Chainate, W., Thapatsuwan, P.: Exploration of genetic parameters and operators through travelling salesman problem. Science Asia 33, 215–222 (2007)

    Article  Google Scholar 

  25. Pongcharoen, P., Stewardson, D.J., Hicks, C., Braiden, P.M.: Applying designed experiments to optimize the performance of genetic algorithms used for scheduling complex products in the capital goods industry. Journal of Applied Statistic 28, 441–455 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  26. Murata, T., Ishibuchi, H.: Performance evaluation of genetic algorithms for flowshop scheduling problems. In: Proceedings of the First IEEE Conference on Evolutionary Computation, pp. 812–817 (1994)

    Google Scholar 

  27. Murphy, K., Travers, P., Walport, M.: Janeway’s Immunobiology. Garland Science (2007)

    Google Scholar 

  28. Dasgupta, D.: Advances in artificial immune systems. IEEE computational intelligence magazine, 40-49 (November 2006)

    Google Scholar 

  29. TSPLIB. Travelling salesman problem library, http://www.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB95/

  30. Agarwal, R., Tiwari, M.K., Mukherjee, S.K.: Artificial immune system based approach for solving resource constraint project scheduling problem. International Journal of Advanced Manufacturing Technology 34, 584–593 (2007)

    Article  Google Scholar 

  31. Lundy, M., Mees, A.: Convergence of an annealing algorithm. Mathematical Programming 34, 111–124 (1986)

    Article  MATH  MathSciNet  Google Scholar 

  32. Glass, C.A., Potts, C.N.: A comparison of local search methods for flow shop scheduling. Annals of Operations Research 63, 489–509 (1996)

    Article  MATH  Google Scholar 

  33. Azimi, Z.N.: Hybrid heuristics for Examination Timetabling problem. Applied Mathematics and Computation 163, 705–733 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  34. Pongcharoen, P., Promtet, W.: Exploring and determining genetic algorithms parameters through experimental design and analysis. In: Proceedings of the 33rd international conference on computers and industrial engineering, Jeju, Korea (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering ,Faculty of Engineering,  ,  

    Pupong Pongcharoen & Warattapop Chainate

  2. Department of Medicine, Faculty of Medicine, Naresuan University, Pitsanulok, Thailand, 65000

    Sutatip Pongcharoen

Authors
  1. Pupong Pongcharoen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Warattapop Chainate
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sutatip Pongcharoen
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Peter J. Bentley Doheon Lee Sungwon Jung

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Pongcharoen, P., Chainate, W., Pongcharoen, S. (2008). Improving Artificial Immune System Performance: Inductive Bias and Alternative Mutations. In: Bentley, P.J., Lee, D., Jung, S. (eds) Artificial Immune Systems. ICARIS 2008. Lecture Notes in Computer Science, vol 5132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85072-4_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-85072-4_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-85071-7

  • Online ISBN: 978-3-540-85072-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation