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Advances in Applied Self-Organizing Systems pp 295–319Cite as

Immune System Support for Scheduling

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

Abstract

Haven’t there been enough approaches to scheduling problems? In terms of variety the answer might be ‘yes’. However, the answer is not as straightforward in terms of effectiveness. In many scheduling problems, it is highly improbable, if not impossible, to obtain optimal schedules within a reasonable amount of time in spite of adopting a wide range of approaches, including evolutionary computation (EC), artificial neural networks (ANN), fuzzy systems (FS), simulated annealing (SA) and Tabu search (TS). In recent years attention has been drawn to another biologically-inspired computing paradigm called artificial immune systems (AIS). An AIS abstracts and models the principles and processes of the biological immune system in order to effectively tackle challenging problems in dynamic environments. Major AIS models include negative selection, clonal selection, immune networks and more recently danger theory (Garrett in Evol. Comput. 13(2):145–177, 2005). There are some similarities between these principles and processes in the immune system and those found in other nature-inspired computing approaches, EC and ANN in particular. However, there are also substantial differences. In particular, adaptive cloning and mutation processes make AIS distinctive and useful.

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References

  • Aickelin, U., & Cayzer, S. (2002). The danger theory and its application to artificial immune systems. In J. Timmis & P. J. Bentley (Eds.), Proceedings of the first international conference on artificial immune systems (ICARIS) (pp. 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., & McLeod, J. (2003). Danger theory: the link between AIS and IDS? In Lecture notes in computer science: Vol. 2787. Proceedings of the second international conference on artificial immune systems (ICARIS) (pp. 147–155). Berlin: Springer.

    Chapter  Google Scholar 

  • Ayara, M., Timmis, J., de Lemos, R., de Castro, L., & Duncan, R. (2002). Negative selection: how to generate detectors. In J. Timmis & P. J. Bentley (Eds.), Proceedings of the first international conference on artificial immune systems (ICARIS) (pp. 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. In Proceedings of the 12th USENIX conference on system administration (pp. 283–298). Boston: USENIX Association.

    Google Scholar 

  • Burnet, F. M. (1959). The clonal selection theory of acquired immunity. Cambridge: Cambridge University Press.

    Google Scholar 

  • Coello Coello, C. A., Rivera, D. C., & Cortés, N. C. (2003). Use of an artificial immune system for job shop scheduling. In Lecture notes in computer science: Vol. 2787/2003. Proceedings of the second international conference on artificial immune systems (ICARIS) (pp. 1–10). Berlin: Springer.

    Chapter  Google Scholar 

  • Costa, A. M., Vargas, P. A., Von Zuben, F. J., & Franca, P. M. (2002). Makespan minimization on parallel processors: an immune-based approach. In Proceedings of the 2002 congress on evolutionary computation (CEC’02) (Vol. 1, pp. 920–925). Washington: IEEE.

    Google Scholar 

  • Cutello, V., & Nicosia, G. (2002). Multiple learning using immune algorithms. In Fourth international conference on recent advances in soft computing (RASC-2002) (pp. 102–107). Nottingham, UK. Berlin: Springer.

    Google Scholar 

  • D’haeseleer, P., Forrest, S., & Helman, P. (1996). An immunological approach to change detection: algorithms, analysis and implications. In Proceedings of IEEE symposium on security and privacy (pp. 132–143). Oakland: IEEE.

    Google Scholar 

  • Dasgupta, D., Cao, Y., & Yang, C. (1999). An immunogenetic approach to spectra recognition. In W. Banzhaf, J. M. Daida, A. E. Eiben, M. H. Garzon, V. Honavar, M. J. Jakiela, & R. E. Smith (Eds.), Proceedings of the genetic and evolutionary computation conference (GECCO) (pp. 149–155). San Francisco: Morgan Kaufmann.

    Google Scholar 

  • Dasgupta, D., Krishna Kumar, K., Wong, D., & Berry, M. (2004). Negative selection algorithm for aircraft fault detection. In Lecture notes in computer science: Vol. 3239. Proceedings of the third international conference on artificial immune systems (ICARIS) (pp. 1–13). Berlin: Springer.

    Chapter  Google Scholar 

  • de Castro, L. N., & Timmis, J. (2002). Artificial immune systems: a new computational intelligence approach. London: Springer.

    MATH  Google Scholar 

  • de Castro, L. N., & Von Zuben, F. J. (2001). AINET: an artificial immune network for data analysis. In H. A. Abbass, R. A. Sarker, & C. S. Newton (Eds.), Data mining: a heuristic approach (pp. 231–259). Hershey: Idea Group. Chap. XII.

    Chapter  Google Scholar 

  • de Castro, L. N., & 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., & Doyen, A. (2004). A new approach to solve hybrid flow shop scheduling problems by artificial immune system. Future Generations Computer Systems, 20(6), 1083–1095.

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  • Feng, Y.-J., & Feng, Z.-R. (2004). An immunity-based ant system for continuous space multi-modal function optimization. In Proceedings of international conference on machine learning and cybernetics (Vol. 2, pp. 1050–1054). Washington: IEEE.

    Google Scholar 

  • Forrest, S., Perelson, A. S., Allen, L., & Cherukuri, R. (1994). Self-nonself discrimination in a computer. In Proceedings of IEEE symposium research in security and privacy (pp. 202–212). Washington: IEEE.

    Google Scholar 

  • Gao, X. Z., Ovaska, S. J., Wang, X., & Chow, M.-Y. (2004). Neural networks-based negative selection algorithm with applications in fault diagnosis. In Proceedings of international conference on systems, man and cybernetics (Vol. 4, pp. 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., & Cannady, J. (2004). A self-adaptive negative selection approach for anomaly detection. In Proceedings of congress on evolutionary computation (CEC 04) (Vol. 2, pp. 1561–1568).

    Google Scholar 

  • Gonzalez, F., Dasgupta, D., & Kozma, R. (2002). Combining negative selection and classification techniques for anomaly detection. In Proceedings of congress on evolutionary computation (CEC’02) (Vol. 1, pp. 705–710).

    Google Scholar 

  • Grama, A., Gupta, A., Karypis, G., & Kumar, V. (2003). Introduction to parallel computing (2nd ed.). Boston: Addison Wesley.

    Google Scholar 

  • Hajela, P., & Yoo, J. S. (1999). Immune network modelling in design optimization. In D. Corne, M. Dorigo, & F. Glover (Eds.), New ideas in optimization (pp. 203–215). London: McGraw-Hill.

    Google Scholar 

  • Hofmeyr, S., & 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., & Bentley, P. J. (2001). Towards an artificial immune system for network intrusion detection: an investigation of clonal selection with a negative selection operator. In Proceedings of congress on evolutionary computation (CEC’01) (Vol. 2, pp. 1244–1252). Washington: IEEE.

    Google Scholar 

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

    Article  MATH  Google Scholar 

  • Krishna Kumar, K., Satyadas, A., & Neidhoefer, J. (1995). An immune system framework for integrating computational intelligence paradigms with applications to adaptive control. In M. Palaniswami, Y. Attikiouzel, R. J. Marks II, D. Fogel, & T. Fukuda (Eds.), Computational intelligence a dynamic system perspective (pp. 32–45). New York: IEEE Press.

    Google Scholar 

  • Kwok, Y. K., & Ahmad, I. (1998). Benchmarking the task graph scheduling algorithms. In Proceedings of first merged international parallel symposium/Symposium on parallel and distributed processing (IPPS/SPDP ’98) (pp. 531–537). Washington: IEEE.

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  • Mori, K., Tsukiyama, M., & Fukuda, T. (1998). Adaptive scheduling system inspired by immune system. In Proceedings of international conference on systems, man, and cybernetics (Vol. 4, pp. 3833–3837). Washington: IEEE.

    Google Scholar 

  • Ong, Z. X., Tay, J. C., & Kwoh, C. K. (2005). Applying the clonal selection principle to find flexible job-shop schedules. In Proceedings of international conference on artificial immune systems (ICARIS) (pp. 442–455). Berlin: Springer.

    Chapter  Google Scholar 

  • Stibor, T., Timmis, J., & Eckert, C. (2005). On the appropriateness of negative selection defined over Hamming shape-space as a network intrusion detection system. In Proceedings of congress on evolutionary computation (Vol. 2, pp. 995–1002). Washington: IEEE.

    Google Scholar 

  • Swiecicka, A., Seredynski, F., & 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., & Neal, M. J. (2000). A resource limited artificial immune system for data analysis. In Proceedings of ES 2000 (pp. 19–32). Berlin: Springer.

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  • Zuo, X.-Q., & Fan, Y.-S. (2005). Solving the job shop scheduling problem by an immune algorithm. In Proceedings of international conference on machine learning and cybernetics (Vol. 6, pp. 3282–3287). Washington: IEEE.

    Google Scholar 

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Authors and Affiliations

  1. School of Information Technologies, The University of Sydney, Sydney, NSW, Australia

    Young Choon Lee & Albert Y. Zomaya

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  1. Young Choon Lee
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  2. Albert Y. Zomaya
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Correspondence to Young Choon Lee .

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Editors and Affiliations

  1. ICT Centre, CSIRO, Marsfield, New South Wales, Australia

    Mikhail Prokopenko

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Lee, Y.C., Zomaya, A.Y. (2013). 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/10.1007/978-1-4471-5113-5_11

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  • DOI: https://doi.org/10.1007/978-1-4471-5113-5_11

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-5112-8

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