Operator Learning for a Problem Class in a Distributed Peer-to-Peer Environment

  • Márk Jelasity
  • Mike Preuβ
  • A. E. Eiben
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2439)


This paper discusses a promising new research direction, the automatic learning of algorithm components for problem classes. We focus on the methodology of this research direction. As an illustration, a mutation operator for a special class of subset sum problem instances is learned. The most important methodological issue is the emphasis on the generalisability of the results. Not only a methodology but also a tool is proposed. This tool is called DRM (distributed resource machine), developed as part of the DREAM project, and is capable of running distributed experiments on the Internet making a huge amount of resources available to the researcher in a robust manner. It is argued that the DRM is ideally suited for algorithm learning.


Problem Instance Mutation Operator Problem Class Operator Learn Algorithm Component 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    M. J. Coster, A. Joux, B. A. LaMacchia, A. M. Odlyzko, C.-P. Schnorr, and J. Stern. An improved low-density subset sum algorithm. Computational Complexity, 2:111–128, 1992.zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    A. Demers, D. Greene, C. Hauser, W. Irish, J. Larson, S. Shenker, H. Sturgis, D. Swinehart, and D. Terry. Epidemic algorithms for replicated database management. In Proceedings of the 6th Annual ACM Symposium on Principles of Distributed Computing (PODC’87), pages 1–12, Vancouver, Aug. 1987. ACM.Google Scholar
  3. 3.
    A. E. Eiben, R. Hinterding, and Z. Michalewicz. Parameter control in evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 3(2):124–141, July 1999.CrossRefGoogle Scholar
  4. 4.
    Á. E. Eiben and M. Jelasity. A critical note on experimental research methodology in EC. In Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002) [8], pages 582–587.Google Scholar
  5. 5.
    A. E. Eiben and C. A. Schippers. Multi-parent’s niche: n-ary crossovers on NKlandscapes. In W. Ebeling, I. Rechenberg, H.-P. Schwefel, and H.-M. Voigt, editors, Parallel Problem Solving from Nature— PPSN IV, volume 1141 of Lecture Notes in Computational Science, pages 319–328. Springer-Verlag, 1996.Google Scholar
  6. 6.
    I. Foster and C. Kesselman, editors. The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann Publishers, 1999.Google Scholar
  7. 7.
    J. J. Grefenstette. Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics, 16(1):122–128, 1986.CrossRefGoogle Scholar
  8. 8.
    IEEE. Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002). IEEE Press, 2002.Google Scholar
  9. 9.
    M. Jelasity. A wave analysis of the subset sum problem. In T. Bäck, editor, Proceedings of the Seventh International Conference on Genetic Algorithms, pages 89–96, San Francisco, California, 1997. Morgan Kaufmann.Google Scholar
  10. 10.
    M. Jelasity, M. Preuβ, M. van Steen, and B. Paechter. Maintaining connectivity in a scalable and robust distributed environment. In H. E. Bal, K.-P. Löhr, and A. Reinefeld, editors, Proceedings of the 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGrid2002), pages 389–394, Berlin, Germany, 2002.Google Scholar
  11. 11.
    M. Jelasity, M. Preuβ, and B. Paechter. A scalable and robust framework for distributed applications. In Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002) [8], pages 1540–1545.Google Scholar
  12. 12.
    M. Jelasity. Towards automatic domain knowledge extraction for evolutionary heuristics. In M. Schoenauer, K. Deb, G. Rudolph, X. Yao, E. Lutton, J. J. Merelo, and H.-P. Schwefel, editors, Parallel Problem Solving from Nature— PPSN VI, volume 1917 of Lecture Notes in Computational Science, pages 755–764. Springer-Verlag, 2000.Google Scholar
  13. 13.
    B. Paechter, T. Bäck, M. Schoenauer, M. Sebag, A. E. Eiben, J. J. Merelo, and T. C. Fogarty. A distributed resoucre evolutionary algorithm machine (DREAM). In Proceedings of the 2000 Congress on Evolutionary Computation (CEC 2000), pages 951–958. IEEE, IEEE Press, 2000.Google Scholar
  14. 14.
    M. Pelikan, D. E. Goldberg, and F. Lobo. A survey of optimization by building and using probablistic models. Technical Report 99018, Illinois Genetic Algorithms Laboratory, 1999.Google Scholar
  15. 15.
    R. G. Reynolds. Cultural algorithms: Theory and applications. In D. Corne, M. Dorigo, and F. Glover, editors, New Ideas in Optimization, Advanced Topics in Computer Science, pages 367–377. McGrow-Hill, 1999.Google Scholar
  16. 17.
    A. S. Tanenbaum and M. van Steen. Distributed Systems: Principles and Paradigms. Prentice Hall, 2002.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Márk Jelasity
    • 1
  • Mike Preuβ
    • 2
  • A. E. Eiben
    • 1
  1. 1.Free University of AmsterdamAmsterdamThe Netherlands
  2. 2.University of DortmundDortmundGermany

Personalised recommendations