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Task scheduling with use of classifier systems

  • Franciszek Seredyński
Novel Techniques and Applications of Evolutionary algorithms
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1305)

Abstract

A new approach to develop parallel and distributed algorithms for scheduling tasks in parallel computers with use of learning machines is proposed. Coevolutionary multi-agent systems with game theoretical model of interaction between agents serve as a theoretical framework for the approach. Genetic-algorithms based learning machines called classifier systems are used as players in a game. Experimental study of such a system shows its self-organizing features and the ability of emergent behavior. Following this approach a parallel and distributed scheduler is described. Results of the experimental study of the scheduler are presented.

Keywords

Genetic Algorithm Payoff Function Classifier System System Graph Multiprocessor System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    I. Ahmad, (ed.), Special Issue on Resource Management in Parallel and Distributed Systems with Dynamic Scheduling: Dynamic Scheduling, Concurrency: Practice and Experience, 7(7), 1995.Google Scholar
  2. 2.
    I. Ahmad and Y. Kwok, A Parallel Approach for Multiprocessing Scheduling, 9th Int. Parallel Processing Symposium, Santa Barbara, CA, April 25–28,1995Google Scholar
  3. 3.
    R. Axelrod, The Evolution of Strategies in the Iterated Prisoners' Dilemma. In Davis L. (Ed.). Genetic Algorithms and Simulated Annealing. London, Pitman, 1987Google Scholar
  4. 4.
    J. Blaiewicz, K.H. Ecker, G. Schmidt, J. Węglarz, Scheduling in Computer and Manufacturing Systems, Springer, 1994Google Scholar
  5. 5.
    L. B. Booker, D. E. Goldberg and J. H. Holland, Classifier Systems and Genetic Algorithms, Artificial Intelligence, 40, 1989Google Scholar
  6. 6.
    R. Bowden and S. F. Bullington, An Evolutionary Algorithm for Discovering Manufacturing Control Strategies, in Evolutionary Algorithms in Management Applications, J. Biethahn and V. Nissen (Eds.), Springer, 1995Google Scholar
  7. 7.
    M. Dorigo and U. Schnepf, Genetic-based Machine Learning and Behavior-based Robotics: a New Synthesis, IEEE Trans. on Systems, Man, and Cybernetics, v. 23, 1993Google Scholar
  8. 8.
    H. El-Rewini and T. G. Lewis, “Scheduling Parallel Program Tasks onto Arbitrary Target Machines”, J. of Parallel and Distributed Computing 9, 138–153, 1990Google Scholar
  9. 9.
    H. El-Rewini, T. G. Lewis, H. H. Ali, Task Scheduling in Parallel and Distributed Systems, PTR Prentice Hall, 1994.Google Scholar
  10. 10.
    D. B. Fogel, Evolving Behaviors in the Iterated Prisoner's Dilemma, Evolutionary Computation. vol. 1. N 1, 1993Google Scholar
  11. 11.
    D. E. Goldberg Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, MA, 1989Google Scholar
  12. 12.
    S. Matwin, T. Szapiro and K. Haigh, Genetic Algorithms Approach to a Negotiation Support System, IEEE Trans. on Systems, Man, and Cybernetics, v. 21, N1, 1991Google Scholar
  13. 13.
    M. Schwehm, T. Walter, Mapping and Scheduling by Genetic Algorithms, CONPAR 94-VAPPVI, B. Buchberger and J. Volkert (eds.), LNCS 854, Springer, 1994Google Scholar
  14. 14.
    F. Seredynski, Loosely Coupled Distributed Genetic Algorithms, Parallel Problem Solving from Nature-PPSN III, Y. Davidor, H.-P. Schwefel and R. Miinner (eds.), LNCS 866, Springer, 1994Google Scholar
  15. 15.
    F. Seredynski and P. Frejlak, Genetic Algorithms Implementation of Process Migration Strategies, in Parallel Computing: Trends and Applications, G. R. Joubert, D. Trystram, F. J. Peters and D. J. Evans (eds.), Elsevier, 1994.Google Scholar
  16. 16.
    F. Seredynski, P. Cichosz and G. P. Klebus, Learning Classifier Systems in MultiAgent Environments, First IEE/IEEE Int. Conf. on Genetic Algorithms in Engineering Systems: Innovations and Applications (GALESIA'95), Shefield, UK, Sept. 11–14, 1995, IEE 1995.Google Scholar
  17. 17.
    F. Seredynski, Coevolutionary Game Theoretic Multi-Agent Systems, in Foundations of Intelligent Systems, Z. W. Ras and M. Michalewicz (eds.), LNAI 1079, Springer, 1996Google Scholar
  18. 18.
    B. Shirazi, A.R. Hurson and K.M. Kavi (eds.), Scheduling and Load Balancing in Parallel and Distributed Systems, IEEE Computer Society Press, 1995Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Franciszek Seredyński
    • 1
  1. 1.Institute of Computer SciencePolish Academy of SciencesWarsawPoland

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