Evolutionary Pointillist Modules: Evolving Assemblages of 3D Objects

  • Penousal Machado
  • Fernando Graca
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4974)


A novel evolutionary system for the creation of assemblages of three dimensional (3D) objects is presented. The proposed approach allows the evolution of the type, size, rotation and position of 3D objects that are placed on a virtual canvas, constructing a non-photorealistic transformation of a source image. The approach is thoroughly described, giving particular emphasis to genotype–phenotype mapping, and to the alternative object placement strategies. The experimental results presented highlight the differences between placement strategies, and show the potential of the approach for the production of large-scale artworks.


Evolutionary Art Evolutionary Image Filters Non- Photorealistic Rendering Assemblage 


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  1. 1.
    Ross, B.J., Ralph, W., Hai, Z.: Evolutionary image synthesis using a model of aesthetics. In: Yen, G.G., Lucas, S.M., Fogel, G., Kendall, G., Salomon, R., Zhang, B.T., Coello, C.A.C., Runarsson, T.P. (eds.) Proceedings of the 2006 IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada, pp. 1087–1094. IEEE Press, Los Alamitos (2006)CrossRefGoogle Scholar
  2. 2.
    Neufeld, C., Ross, B., Ralph, W.: The evolution of artistic filters. In: Romero, J., Machado, P. (eds.) The Art of Artificial Evolution, Springer, Heidelberg (2007) (in Press)Google Scholar
  3. 3.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Natural Selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  4. 4.
    Lewis, M.: Aesthetic video filter evolution in an interactive real-time framework. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 409–418. Springer, Heidelberg (2004)Google Scholar
  5. 5.
    Machado, P., Dias, A., Cardoso, A.: Learning to colour greyscale images. The Interdisciplinary Journal of Artificial Intelligence and the Simulation of Behaviour – AISB Journal 1, 209–219 (2002)Google Scholar
  6. 6.
    Yip, C.: Evolving Image Filters. Master’s thesis, Imperial College of Science, Technology, and Medicine (2004)Google Scholar
  7. 7.
    Collomosse, J.P., Hall, P.M.: Genetic paint: A search for salient paintings. In: Applications of Evolutionary Computing, EvoWorkshops 2005, Lausanne, Switzerland, pp. 437–447 (2005)Google Scholar
  8. 8.
    Collomosse, J.P.: Supervised genetic search for parameter selection in painterly rendering. In: Applications of Evolutionary Computing, EvoWorkshops 2006, Budapest, Hungary, pp. 599–610 (2006)Google Scholar
  9. 9.
    Sims, K.: Artificial evolution for computer graphics. ACM Computer Graphics 25, 319–328 (1991)CrossRefGoogle Scholar
  10. 10.
    Tannenbaum, T., Wright, D., Miller, K., Livny, M.: Condor – a distributed job scheduler. In: Sterling, T. (ed.) Beowulf Cluster Computing with Linux, MIT Press, Cambridge (2001)Google Scholar
  11. 11.
    Velho, L., de Miranda Gomes, J.: Digital halftoning with space filling curves. SIGGRAPH Comput. Graph. 25, 81–90 (1991)CrossRefGoogle Scholar
  12. 12.
    Shiraishi, M., Yamaguchi, Y.: An algorithm for automatic painterly rendering based on local source image approximation. In: NPAR 2000: Proceedings of the 1st international symposium on Non-photorealistic animation and rendering, pp. 53–58. ACM, New York (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Penousal Machado
    • 1
  • Fernando Graca
    • 1
  1. 1.CISUC, Department of Informatics EngineeringUniversity of Coimbra, Polo II of the University of CoimbraCoimbraPortugal

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