Skip to main content
SpringerLink
Log in

Immune System Support for Scheduling

  • Chapter
  • First Online:
Advances in Applied Self-organizing Systems

Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

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 109.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

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.

References

  • Aickelin, U., and Cayzer, S. (2002). The danger theory and its application to artificial immune systems. In Timmis, J. and Bentley, P. J., editors, Proceedings of the First International Conference on Artificial Immune Systems (ICARIS), pages 141–148. University of Kent at Canterbury, September 2002. University of Kent at Canterbury Printing Unit.

    Google Scholar 

  • Aickelin, U., Bentley, P., Cayzer, S., Kim, J., and McLeod, J. (2003). Danger theory: The link between AIS and IDS? Proceedings of the Second International Conference on Artificial Immune Systems (ICARIS), volume 2787 of Lecture Notes in Computer Science, pages 147–155. Edinburgh, UK, Springer.

    Google Scholar 

  • Ayara, M., Timmis, J., de Lemos, R., de Castro, L., and Duncan, R. (2002). Negative selection: How to generate detectors. In Timmis, J., and Bentley, P. J., editors, Proceedings of the First International Conference on Artificial Immune Systems (ICARIS), pages 89–98. University of Kent at Canterbury, September 2002. University of Kent at Canterbury Printing Unit.

    Google Scholar 

  • Bersini, H. (2002). The immune and the chemical crossover. IEEE Transactions on Evolutionary Computation, 6(3):306–313.

    Article  Google Scholar 

  • Burgess, M. (1998). Computer immunology. Proceedings of the 12th USENIX Conference on System Administration, pages 283–298. Boston, Massachusetts, USA, USENIX Association.

    Google Scholar 

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

    Book  Google Scholar 

  • Coello Coello, C.A., Rivera, D. C. and Cortés, N. C. (2003). Use of an artificial immune system for job shop scheduling. Proceedings of the Second International Conference on Artificial Immune Systems (ICARIS), volume 2787/2003 of Lecture Notes in Computer Science, pages 1–10. Edinburgh, UK, Springer.

    Google Scholar 

  • Costa, A. M., Vargas, P. A., Von Zuben, F. J. and Franca, P. M. (2002). Makespan minimization on parallel processors: An immune-based approach. Proceedings of the 2002 Congress on Evolutionary Computation (CEC’02), volume 1, pages 920–925. IEEE Computer Society, Washington DC, USA.

    Google Scholar 

  • Cutello, V. and Nicosia, G. (2002). Multiple learning using immune algorithms. In Fourth International Conference on Recent Advances in Soft Computing (RASC-2002), pages 102-107. Nottingham. Springer-Verlag, Berlin.

    Google Scholar 

  • D’haeseleer, P., Forrest, S., and Helman, P. (1996). An immunological approach to change detection: Algorithms, analysis and implications. Proceedings of IEEE Symposium on Security and Privacy, pages 132–143. Oakland, CA, USA, IEEE Computer Society.

    Google Scholar 

  • Dasgupta, D., Cao, Y. and Yang, C. (1999). An immunogenetic approach to spectra recognition. In: Banzhaf, W., Daida, J. M., Eiben, A. E., Garzon, M. H., Honavar, V., Jakiela, M. J., and Smith, R. E., editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pages 149–155. Morgan Kaufmann, San Fransisco, LA, USA.

    Google Scholar 

  • Dasgupta, D., KrishnaKumar, K., Wong, D. and Berry, M. (2004). Negative selection algorithm for aircraft fault detection. Proceedings of the Third International Conference on Artificial Immune Systems (ICARIS), volume 3239 of Lecture Notes in Computer Science, pages 1–13. Springer Berlin.

    Google Scholar 

  • de Castro, L. N., and Timmis, J. (2002). Artificial Immune Systems: A New Computational Intelligence Approach. Springer-Verlag, London.

    MATH  Google Scholar 

  • de Castro, L. N., and Von Zuben, F. J. (2001). AINET: An artificial immune network for data analysis. In Abbass, H. A., Sarker, R. A., and Newton, C. S., editors, Data Mining: A Heuristic Approach, chapter XII, pages 231–259. Idea Group Publishing Hershey, PA, USA.

    Google Scholar 

  • de Castro, L. N., and Von Zuben, F. J. (2002). Learning and optimization using the clonal selection principle. IEEE Transactions on Evolutionary Computation, 6(3):239–251.

    Article  Google Scholar 

  • Engin, O., and Doyen, A. (2004). A new approach to solve hybrid flow shop scheduling problems by artificial immune system. Future Generation Computer Systems, 20(6):1083–1095.

    Article  Google Scholar 

  • Esponda, F., Ackley, E. S., Forrest, S. and Helman, P. (2005). On-line negative databases. International Journal of Unconventional Computing, 1(3):201–220.

    Google Scholar 

  • Farmer, J., Packard, N., and Perelson, A. (1986). The immune system, adaptation and machine learning. Physica D, 22:187–204.

    Article  MathSciNet  Google Scholar 

  • Feng, Y.-J., and Feng, Z.-R. (2004). An immunity-based ant system for continuous space multi-modal function optimization. Proceedings of International Conference on Machine Learning and Cybernetics, volume 2, pages 1050–1054. IEEE Computer Society, Washington DC, USA.

    Google Scholar 

  • Forrest, S., Perelson, A. S., Allen, L. and Cherukuri, R. (1994). Self-nonself discrimination in a computer. Proceedings of IEEE Symposium Research in Security and Privacy, pages 202–212. IEEE Computer Society, Washington DC, USA.

    Google Scholar 

  • Gao, X.Z., Ovaska, S.J., Wang, X. and Chow, M.-Y. (2004). Neural networks-based negative selection algorithm with applications in fault diagnosis. Proceedings of International Conference on Systems, Man and Cybernetics, volume 4, pages 3408–3414.

    Google Scholar 

  • Garrett, S. (2005). How Do We Evaluate Artificial Immune Systems?. Evolutionary Computation, 13(2):145–177.

    Article  Google Scholar 

  • Gonzales, L.J., and Cannady, J. (2004). A self-adaptive negative selection approach for anomaly detection. Proceedings of Congress on Evolutionary Computation (CEC 04), volume 2, pages 1561–1568.

    Google Scholar 

  • Gonzalez, F., Dasgupta, D. and Kozma, R. (2002). Combining negative selection and classification techniques for anomaly detection. Proceedings of Congress on Evolutionary Computation (CEC’02), volume 1, pages 705–710.

    Google Scholar 

  • Grama, A., Gupta, A., Karypis, G. and Kumar, V. (2003). Introduction to Parallel Computing. 2nd Edition, Addison Wesley, Boston, MA, USA.

    MATH  Google Scholar 

  • Hajela, P., and Yoo. J. S. (1999). Immune network modelling in design optimization. In Corne, D., Dorigo, M., and Glover, F., editors, New Ideas in Optimization, pages 203–215. McGraw Hill, London.

    Google Scholar 

  • Hofmeyr, S., and Forrest, S. (2000). Architecture for the artificial immune system. Evolutionary Computation, 8(4):443–473.

    Article  Google Scholar 

  • Jerne, N. (1974). Towards a network theory of the immune system. Annals of Immunology, 125:373–389.

    Google Scholar 

  • Kim, J., and Bentley, P.J. (2001). Towards an artificial immune system for network intrusion detection: An investigation of clonal selection with a negative selection operator. Proceedings of Congress on Evolutionary Computation (CEC’01), volume 2, pages 1244–1252. IEEE Computer Society, Washington DC, USA.

    Google Scholar 

  • King, R. L., Russ, S. H., Lambert, A. B., and Reese, D. S. (2001). An artificial immune system model for intelligent agents. Future Generation Computer Systems, 17(4):335–343.

    Article  Google Scholar 

  • KrishnaKumar, K., Satyadas, A. and Neidhoefer, J. (1995). An immune system framework for integrating computational intelligence paradigms with applications to adaptive control. In Palaniswami, M., Attikiouzel, Y., Marks, R.J., II, Fogel, D., and Fukuda T., editors, Computational Intelligence A Dynamic System Perspective, pages 32–45, IEEE.

    Google Scholar 

  • Kwok, Y. K., and Ahmad, I. (1998). Benchmarking the task graph scheduling algorithms. Proceedings of First Merged International Parallel Symposium/Symposium on Parallel and Distributed Processing (IPPS/SPDP ‘98), pages 531–537. IEEE Computer Society, Washington DC, USA.

    Google Scholar 

  • Matzinger, P. (2002). The danger model: A renewed sense of self. Science, 296:301–305.

    Article  Google Scholar 

  • Mori, K., Tsukiyama, M. and Fukuda, T. (1998). Adaptive scheduling system inspired by immune system. Proceedings of International Conference on Systems, Man, and Cybernetics, volume 4, pages 3833–3837. IEEE Computer Society, Washington DC, USA.

    Google Scholar 

  • Ong, Z. X, Tay, J. C. and Kwoh, C. K. (2005). Applying the clonal selection principle to find flexible job-shop schedules. Proceedings of International Conference on Artificial Immune Systems (ICARIS), pages 442–455, Springer-Verlag Berlin.

    Google Scholar 

  • Stibor, T., Timmis, J. and Eckert, C. (2005). On the appropriateness of negative selection defined over Hamming shape-space as a network intrusion detection system. Proceedings of Congress on Evolutionary Computation, volume 2, pages 995–002. IEEE Computer Society, Washington DC, USA.

    Google Scholar 

  • Swiecicka, A., Seredynski, F., and Zomaya, A.Y. (2006). Multiprocessor scheduling and rescheduling with use of cellular automata and artificial immune system support. IEEE Transactions on Parallel and Distributed Systems, 17(3):253–262.

    Article  Google Scholar 

  • Timmis, J., and Neal, M. J. (2000). A resource limited artificial immune system for data analysis. Proceedings of ES 2000, pages 19–32, Springer.

    Google Scholar 

  • Topcuoglu, H., Hariri, S. and Wu, M. (2002). Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Transactions on Parallel and Distributed Systems, 13(3):260–274.

    Article  Google Scholar 

  • Varela, F. J., and Coutinho, A. (1991). Second generation immune networks. Immunology Today, 12(55):159–166.

    Article  Google Scholar 

  • Wierzchon, S. T. (2000). Discriminative power of the receptors activated by k-contiguous bits rule. Journal of Computer Science and Technology, Special Issue on Research Computer Science, 1(3):1–13.

    Google Scholar 

  • Zuo, X.-Q., and Fan, Y.-S. (2005). Solving the job shop scheduling problem by an immune algorithm. Proceedings of International Conference on Machine Learning and Cybernetics, volume 6, pages 3282–3287. IEEE Computer Society, Washington DC, USA.

    Google Scholar 

Download references

Authors
  1. Young Choon Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Albert Y. Zomaya
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia

    Mikhail Prokopenko PhD, MA, MSc (Hon)

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag London Limited

About this chapter

Cite this chapter

Lee, Y.C., Zomaya, A.Y. (2008). Immune System Support for Scheduling. In: Prokopenko, M. (eds) Advances in Applied Self-organizing Systems. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-84628-982-8_11

Download citation

  • DOI: https://doi.org/10.1007/978-1-84628-982-8_11

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84628-981-1

  • Online ISBN: 978-1-84628-982-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation