A Memetic Island Model for Discrete Tomography Reconstruction

  • Marco Cipolla
  • Giosuè Lo Bosco
  • Filippo Millonzi
  • Cesare Valenti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6857)


Soft computing is a term indicating a coalition of methodologies, and its basic dogma is that, in general, better results can be obtained through the use of constituent methodologies in combination, rather than in a stand alone mode. Evolutionary computing belongs to this coalition, and thus memetic algorithms. Here, we present a combination of several instances of a recently proposed memetic algorithm for discrete tomography reconstruction, based on the island model parallel implementation. The combination is motivated by the fact that, even though the results of the recently proposed approach are finally better and more robust compared to other approaches, we advised that its major drawback was the computational time. The underlying combination strategy consists in separated populations of agents evolving by means of different processes which share some individuals, from time to time. Experiments were performed to test the benefits of this paradigm in terms of computational time and correctness of the solutions.


Genetic Algorithm Memetic Algorithm Generic Image Island Model Discrete Tomography 
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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  1. 1.
    Kak, A.C., Slaney, M.: Principles of Computerized Tomography Imaging. Society for Industrial Mathematics (2001)Google Scholar
  2. 2.
    Herman, G.T., Kuba, A. (eds.): Discrete Tomography: Foundations, Algorithms, and Applications. Birkhäuser, Basel (1999)zbMATHGoogle Scholar
  3. 3.
    Gale, D.: A theorem on flows in networks. Pacific Journal of Mathematics 7, 1073–1082 (1957)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Ryser, H.J.: Combinatorial properties of matrices of zeros and ones. Canadian Journal of Mathematics 9, 371–377 (1957)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Gardner, R.J., Gritzmann, P., Prangenberg, D.: On the computational complexity of reconstructing lattice sets from their X-rays. Discrete Mathematics 202, 45–71 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Wang, B., Zhang, F.: On the precise number of (0,1)-matrices in U(R,S). Discrete Mathematics 187, 211–220 (1998)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Frosini, A., Nivat, M., Vuillon, L.: An introductive analysis of periodical discrete sets from a tomographical point of view. Theoretical Computer Science 347(1–2), 370–392 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Svalbe, I., van der Spek, D.: Reconstruction of tomographic images using analog projections and the digital Radon transform. Linear Algebra and its Applications 339, 125–145 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Anstee, R.P.: The network flows approach for matrices with given row and column sums. Discrete Mathematics 44, 125–138 (1983)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Balázs, P., Balogh, E., Kuba, A.: Reconstruction of 8-connected but not 4-connected hv-convex discrete sets. Discrete Applied Mathematics 147, 149–168 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Herman, G.T., Kuba, A.: Discrete Tomography: Foundations, Algorithms, and Applications. In: Binary Tomography Using Gibbs Priors, pp. 191–212. Birkhäuser, Basel (1999)Google Scholar
  12. 12.
    Valenti, C.: A genetic algorithm for discrete tomography reconstruction. Genetic Programming and Evolvable Machines 9, 85–96 (2008)CrossRefGoogle Scholar
  13. 13.
    Batenburg, J.K.: An evolutionary algorithm for discrete tomography. Discrete Applied Mathematics 151, 36–54 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Balázs, P., Gara, M.: An Evolutionary Approach for Object-Based Image Reconstruction Using Learnt Priors. In: Salberg, A.-B., Hardeberg, J.Y., Jenssen, R. (eds.) SCIA 2009. LNCS, vol. 5575, pp. 520–529. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  15. 15.
    Moscato, P.: On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Caltech Concurrent Computation Program, C3P Report 826 (1989)Google Scholar
  16. 16.
    Corne, D., Dorigo, M., Glover, F.: New ideas in optimization. McGraw-Hill, New York (1999)Google Scholar
  17. 17.
    Eklund, S.E.: A massively parallel architecture for distributed genetic algorithms. Parallel Computing 30(5–6), 647–676 (2004)CrossRefGoogle Scholar
  18. 18.
    Di Gesù, V., Lo Bosco, G., Millonzi, F., Valenti, C.: A memetic approach to discrete tomography from noisy projections. Pattern Recognition 43(9), 3073–3082 (2010)CrossRefGoogle Scholar
  19. 19.
    Isgró, F., Tegolo, D.: A distributed genetic algorithm for restoration of vertical line scratches. Parallel Computing 34(12), 727–734 (2008)CrossRefGoogle Scholar
  20. 20.
    Ryser, H.J.: Combinatorial mathematics. The carus mathematical monographs. Ch.6, (14). MAA (1963)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Marco Cipolla
    • 1
  • Giosuè Lo Bosco
    • 1
  • Filippo Millonzi
    • 1
  • Cesare Valenti
    • 1
  1. 1.Dipartimento di Matematica e InformaticaUniversità di PalermoItaly

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