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Enhancing a Genetic Algorithm with a Solution Archive to Reconstruct Cross Cut Shredded Text Documents

  • Benjamin Biesinger
  • Christian Schauer
  • Bin Hu
  • Günther R. Raidl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8111)

Abstract

In this work the concept of a trie-based complete solution archive in combination with a genetic algorithm is applied to the Reconstruction of Cross-Cut Shredded Text Documents (RCCSTD) problem. This archive is able to detect and subsequently convert duplicates into new yet unvisited solutions. Cross-cut shredded documents are documents that are cut into rectangular pieces of equal size and shape. The reconstruction of documents can be of high interest in forensic science. Two types of tries are compared as underlying data structure, an indexed trie and a linked trie. Experiments indicate that the latter needs considerably less memory without affecting the run-time. While the archive-enhanced genetic algorithm yields better results for runs with a fixed number of iterations, advantages diminish due to the additional overhead when considering run-time.

Keywords

genetic algorithm solution archive reconstruction 

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References

  1. 1.
    Gusfield, D.: Algorithms on strings, trees, and sequences: computer science and computational biology. Cambridge University Press, New York (1997)CrossRefzbMATHGoogle Scholar
  2. 2.
    Hu, B., Raidl, G.R.: An evolutionary algorithm with solution archives and bounding extension for the generalized minimum spanning tree problem. In: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation (GECCO), pp. 393–400. ACM Press, Philadelphia (2012)Google Scholar
  3. 3.
    Mauldin, M.L.: Maintaining Diversity in Genetic Search. In: National Conference on Artificial Intelligence, vol. 19, pp. 247–250. AAAI, William Kaufmann (1984)Google Scholar
  4. 4.
    Perl, J., Diem, M., Kleber, F., Sablatnig, R.: Strip shredded document reconstruction using optical character recognition. In: 4th International Conference on Imaging for Crime Detection and Prevention 2011 (ICDP 2011), pp. 1–6 (2011)Google Scholar
  5. 5.
    Prandtstetter, M.: Hybrid Optimization Methods for Warehouse Logistics and the Reconstruction of Destroyed Paper Documents. Ph.D. thesis, Vienna University of Technology (2009)Google Scholar
  6. 6.
    Prandtstetter, M., Raidl, G.R.: Meta-heuristics for reconstructing cross cut shredded text documents. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, GECCO 2009, pp. 349–356. ACM Press, New York (2009)Google Scholar
  7. 7.
    Raidl, G.R., Hu, B.: Enhancing genetic algorithms by a trie-based complete solution archive. In: Cowling, P., Merz, P. (eds.) EvoCOP 2010. LNCS, vol. 6022, pp. 239–251. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Ronald, S.: Duplicate genotypes in a genetic algorithm. In: IEEE World Congress on Computational Intelligence, Evolutionary Computation Proceedings, pp. 793–798 (1998)Google Scholar
  9. 9.
    Schauer, C., Prandtstetter, M., Raidl, G.R.: A memetic algorithm for reconstructing cross-cut shredded text documents. In: Blesa, M.J., Blum, C., Raidl, G., Roli, A., Sampels, M. (eds.) HM 2010. LNCS, vol. 6373, pp. 103–117. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Sleit, A., Massad, Y., Musaddaq, M.: An alternative clustering approach for reconstructing cross cut shredded text documents. Telecommunication Systems, 1–11 (2011)Google Scholar
  11. 11.
    Yuen, S.Y., Chow, C.K.: A non-revisiting genetic algorithm. In: IEEE Congress on Evolutionary Computation, CEC 2007, pp. 4583–4590 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Benjamin Biesinger
    • 1
  • Christian Schauer
    • 1
  • Bin Hu
    • 1
  • Günther R. Raidl
    • 1
  1. 1.Institute of Computer Graphics and AlgorithmsVienna University of TechnologyViennaAustria

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