GRASP Algorithm for Optimization of Grids for Multiple Classifier System

Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 73)


In recent years the volume of data used in scientific researches and industry has increased significantly. Distributed computing systems including Grids use the public Internet to share computational resources of research institutions around the world in order to process the data. Due to large data volumes being transferred, network aspects of Grids have become important. In this work we introduce a model of an overlay Grid system, which could be used by the distributed recognition system based on the idea of combining classifiers. We formulate an Integer Programming optimization problem with the objective to minimize the overall cost including processing and data transfer. Next, an effective heuristic algorithm is developed to solve the problem. Results of numerical experiments showing the comparison of the heuristic against solutions provided by CPLEX solver are presented.


Overlay Network Ranking List Grid Network Access Link Restricted Candidate List 
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  1. 1.
    Alexandre, L.A., Campilho, A.C., Kamel, M.: Combining Independent and Unbiased Classifiers Using Weighted Average. In: Proc. of the 15th Internat. Conf. on Pattern Recognition, vol. 2, pp. 495–498 (2000)Google Scholar
  2. 2.
    Alpaydin, E.: Introduction to Machine Learning. The MIT Press, London (2004)Google Scholar
  3. 3.
    Biggio, B., Fumera, G., Roli, F.: Bayesian Analysis of Linear Combiners. In: Haindl, M., Kittler, J., Roli, F. (eds.) MCS 2007. LNCS, vol. 4472, pp. 292–301. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Binato, S., Hery, W.J., Loewenstern, D.M., Resende, M.G.C.: A Greedy Randomized Adaptive Search Procedure for Job Shop Scheduling. In: Essays and Surveys on Metaheuristics, pp. 58–79. Kluwer Academic Publishers, Dordrecht (2002)Google Scholar
  5. 5.
    Freitas, A.A., Lavington, S.H.: Mining Very Large Databases with Parallel Processing. Kluwer Academic Publishers, Boston (1998)zbMATHGoogle Scholar
  6. 6.
    Han, J.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publ. Inc., San Francisco (2005)Google Scholar
  7. 7.
    Festa, P., Resende, M.G.C.: GRASP: An Annotated Bibliography. Essays and surveys on metaheuristics, 325–367 (2002)Google Scholar
  8. 8.
    Foster, I., Kesselman, C.: The Grid 2: Blueprint for a New Computing Infrastructure. Morgan Kaufmann Publishers Inc., San Francisco (2003)Google Scholar
  9. 9.
    ILOG CPLEX 11.0 User’s Manual, France (2007)Google Scholar
  10. 10.
    Leonidas, S., Pitsoulis, L., Resende, M.G.C.: Greedy Randomized Adaptive Search Procedures. In: Handbook of Applied Optimization. Oxford University Press, Oxford (2002)Google Scholar
  11. 11.
    Jain, A.K., Duin, P.W., Mao, J.: Statistical Pattern Recognition: A Review. IEEE Trans. on PAMI 22(1), 4–37 (2000)Google Scholar
  12. 12.
    Kuncheva, L.I., Whitaker, C.J., Shipp, C.A., Duin, R.P.W.: Limits on the Majority Vote Accuracy in Classier Fusion. Pattern Analysis and Applications 6, 22–31 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Kuncheva, L.I.: Combining pattern classifiers: Methods and algorithms. Wiley, Chichester (2004)zbMATHCrossRefGoogle Scholar
  14. 14.
    Magoulès, F., Nguyen, T., Yu, L.: Grid Resource Management: Toward Virtual and Services Compliant Grid Computing. CRC Press, Boca Raton (2009)Google Scholar
  15. 15.
    Nabrzyski, J., Schopf, J., Węglarz, J. (eds.): Grid resource management: state of the art and future trends. Kluwer Academic Publishers, Boston (2004)zbMATHGoogle Scholar
  16. 16.
    Wadenstein, M.: The LHC data stream. Nordic DataGrid Facility (2008)Google Scholar
  17. 17.
    Walkowiak, K., Woźniak, M.: Decision tree induction methods for distributed environment. In: Men-Machine Interactions, Advances in Intelligent and Soft Computing, pp. 201–208 (2009)Google Scholar
  18. 18.
    Zhu, Y., Li, B.: Overlay Networks with Linear Capacity Constraints. IEEE Transactions on Parallel and Distributed Systems 19(2), 159–173 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Wrocław University of TechnologyWrocławPoland

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