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Comparison between Genetic Algorithm and Genetic Programming Performance for Photomosaic Generation

  • Shahrul Badariah Mat Sah
  • Vic Ciesielski
  • Daryl D’Souza
  • Marsha Berry
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5361)

Abstract

Photomosaics are a new form of art in which smaller digital images (known as tiles) are used to construct larger images. Photomosaic generation not only creates interest in the digital arts area but has also attracted interest in the area of evolutionary computing. The photomosaic generation process may be viewed as an arrangement optimisation problem, for a given set of tiles and suitable target to be solved using evolutionary computing. In this paper we assess two methods used to represent photomosaics, genetic algorithms (GAs) and genetic programming (GP), in terms of their flexibility and efficiency. Our results show that although both approaches sometimes use the same computational effort, GP is capable of generating finer photomosaics in fewer generations. In conclusion, we found that the GP representation is richer than the GA representation and offers additional flexibility for future photomosaics generation.

Keywords

Photomosaic Genetic Programming (GP) Genetic Algorithm (GA) 

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References

  1. 1.
    Hinterding, R.: Representation, Mutation and Crossover Issues in Evolutionary Computation. In: Proceeding of Congress of 2000 Evolutionary Computation (CEC 2000), vol. 2, pp. 916–923. IEEE Service Center (2000)Google Scholar
  2. 2.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Massachusetts (1992)zbMATHGoogle Scholar
  3. 3.
    Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Massachusetts (1996)zbMATHGoogle Scholar
  4. 4.
    Ciesielski, V., Berry, M., Trist, K., D’Souza, D.: Evolution of Animated Photomosaics. In: Giacobini, M., et al. (eds.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 498–507. Springer, Heidelberg (2007)Google Scholar
  5. 5.
    Silvers, R., Hawley, M.: Photomosaic. Henry Holt and Company, Inc., New York (1997)Google Scholar
  6. 6.
    Finkelstein, A., Range, M.: Image Mosaic. In: Hersch, R.D., Andre, J., Brown, H. (eds.) RIDT 1998 and EPub 1998. LNCS, vol. 1375, pp. 11–22. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  7. 7.
    Di Blasi, G., Gallo, G., Maria, P.: Smart Ideas for Photomosaic Rendering. In: Proceedings of Eurographics Italian Chapter Conference 2006, Eurographic Association, Catania, Italy (2006)Google Scholar
  8. 8.
    Kim, J., Pellacini, F.: Jigsaw Image Mosaics. ACM Transactions on Graphics (TOG) 21, 657–664 (2006)Google Scholar
  9. 9.
    Park, J.W.: Artistic depiction: Mosaic for Stacktable Objects. In: ACM SIGGRAPH 2004 Sketches SIGGRAPH 2004. ACM, New York (2004)Google Scholar
  10. 10.
    Wijesinghe, G., Mat Sah, S.B., Ciesielski, V.: Grid vs. Arbitrary Placement of Tiles for Generating Animated Photomosaics. In: Proceeding of Congress of 2008 Evolutionary Computation (CEC 2008). IEEE Service Center, Piscataway (2008)Google Scholar
  11. 11.
    Smith, R.E., Goldberg, D.E., Earickson, J.A.: SGA-C: A C-language Implementation of a Simple Genetic Algorithm (1991), http://citeseer.ist.psu.edu/341381.html
  12. 12.
    Sinclair, M.C., Shami, S.H.: Evolving simple agents: Comparing genetic algorithm and genetic programming performance. In: IEE Genetic Algorithms in Engineering Systems: Innovations and Applications, pp. 421–426. IEEE Press, New York (1997)Google Scholar
  13. 13.
    Walker, M., Messom, C.H.: A Comparison of Genetic Programming and Genetic Algorithms for Auto-tuning Mobile Robot Motion Control. In: Proceedings of the First IEEE International Workshop on Electronic Design, Test and Applications (DELTA 2002), pp. 507–509. IEEE Press, New York (2002)CrossRefGoogle Scholar
  14. 14.
    Ebner, M.: On the search space of genetic programming and its relation to nature’s search space. In: Proceedings of the 1999 Congress on Evolutionary Computation, Washington, D.C, July 6-9, vol. 2, pp. 1357–1361. IEEE Press, Los Alamitos (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Shahrul Badariah Mat Sah
    • 1
  • Vic Ciesielski
    • 1
  • Daryl D’Souza
    • 1
  • Marsha Berry
    • 2
  1. 1.School of Computer Science and Information TechnologyRMIT UniversityMelbourneAustralia
  2. 2.School of Creative MediaRMIT UniversityMelbourneAustralia

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