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GPGPU Implementation of Evolutionary Algorithm for Images Clustering

  • Dariusz KoniecznyEmail author
  • Maciej Marcinkowski
  • Paweł B. Myszkowski
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 457)

Abstract

We propose an evolutionary algorithm (EA) usage to image clustering applied to Document Search Engine (DSE). Each document is described by its visual content (including images), preprocessed and clustered by EA. Next, such clusters are core of DSE. However, number of documents and attached images make EA ineffective in such task. Using the natural issue of EA we propose GPGPU (General Purpose Graphic Processing Unit) implementation. The paper describes our proposition, research and results gained in experiments.

Keywords

Document Search Engine Images Clustering Evolutionary Algorithm GPGPU GPU CUDA 

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References

  1. 1.
    Arenas, M.G., Mora, A.M., Romero, G., Castillo, P.A.: GPU Computation in Bioinspired Algorithms: A Review. In: Cabestany, J., Rojas, I., Joya, G. (eds.) IWANN 2011, Part I. LNCS, vol. 6691, pp. 433–440. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Banzhaf, W., Harding, S., Langdon, W.B., Wilson, G.: Accelerating Genetic Programming through Graphics Processing Units. In: Genetic Programming Theory and Practice VI. Genetic and Evolutionary Computation, pp. 1–19. Springer (2009)Google Scholar
  3. 3.
    Cano, A., Zafra, A., Ventura, S.: Speeding up the evaluation phase of GP classification algorithms on GPUs. Soft Computing - A Fusion of Foundations, Methodologies and Applications 16(2), 187–202 (2011)Google Scholar
  4. 4.
    Langdon, W.B.: Graphics processing units and genetic programming: an overview. Soft. Comp. 15, 1657–1669 (2011)CrossRefGoogle Scholar
  5. 5.
    O’Neill, M., Vanneschi, L., Gustafson, S., Banzhaf, W.: Open issues in genetic programming. Genet. Program. Evolvable Mach. 11, 339–363 (2010)CrossRefGoogle Scholar
  6. 6.
    Myszkowski, P.B., Buczek, B.: Growing Hierarchical Self-Organizing Map for searching documents using visual content. In: 6th International Symposium Advances in Artificial Intelligence and Applications (2011)Google Scholar
  7. 7.
    Robilliard, D., Marion-Poty, V., Fonlupt, C.: Genetic programming on graphics processing unit. Genet. Program. Evolvable Mach. 10, 447–471 (2009)CrossRefGoogle Scholar
  8. 8.
    Sharma, D., Collet, P.: GPGPU-Compatible Archive Based Stochastic Ranking Evolutionary Algorithm (G-ASREA) for Multi-Objective Optimization. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI, Part II. LNCS, vol. 6239, pp. 111–120. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Wahib, M., Munawar, A., Munetomo, M., Akama, K.: Optimization of Parallel Genetic Algorithms for nVidia GPUs. In: IEEE Congr. on Ev. Comp. (CEC), pp. 803–811 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Dariusz Konieczny
    • 1
    Email author
  • Maciej Marcinkowski
    • 1
  • Paweł B. Myszkowski
    • 1
  1. 1.Applied Informatics InstituteWrocław University of TechnologyWrocławPoland

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