Evolving Pop Art Using Scalable Vector Graphics

  • E. den Heijer
  • A. E. Eiben
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7247)

Abstract

In this paper we present our findings of our continued investigation into the use of Scalable Vector Graphics as a genotype representation in evolutionary art. In previous work we investigated the feasibility of SVG as a genetic representation for evolutionary art, and found that the representation was very flexible, but that the potential visual output was somewhat limited by the simplicity of our genetic operators. In this paper we extend on this work, and introduce various new, more expressive genetic operators for SVG. We show that SVG is a flexible and powerful representation for evolutionary art, and that the potential visual output is only limited by the design of the genetic operators. With the genetic operators that we describe in this paper, we are able to evolve art that is visually similar to screen printing art and pop art.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    del Acebo, E., Sbert, M.: Benford’s law for natural and synthetic images. In: Neumann et al. [16], pp. 169–176Google Scholar
  2. 2.
    Baluja, S., Pomerleau, D., Jochem, T.: Towards automated artificial evolution for computer-generated images. Connection Science 6, 325–354 (1994)CrossRefGoogle Scholar
  3. 3.
    Bentley, P.J., Corne, D.W. (eds.): Creative Evolutionary Systems. Morgan Kaufmann, San Mateo (2001)Google Scholar
  4. 4.
    Birren, F.: Principles of color: a review of past traditions and modern theories of color harmony. Schiffer Publishing (1987)Google Scholar
  5. 5.
    Collomosse, J.: Evolutionary search for the artistic rendering of photographs. In: Romero and Machado [19], pp. 39–62Google Scholar
  6. 6.
    Stiny, G., Gips, J.: Shape grammars and the generative specification of painting and sculpture. In: Information Processing, pp. 1460–1465 (1972)Google Scholar
  7. 7.
    Gooch, B., Gooch, A.: Non-photorealistic Rendering. A.K. Peters (2001)Google Scholar
  8. 8.
    Greenfield, G.R.: Mathematical building blocks for evolving expressions. In: Sarhangi, R. (ed.) 2000 Bridges Conference Proceedings, pp. 61–70. Central Plain Book Manufacturing, Winfield (2000)Google Scholar
  9. 9.
    den Heijer, E., Eiben, A.E.: Using aesthetic measures to evolve art. In: IEEE Congress on Evolutionary Computation (CEC 2010), July 18-23, IEEE Press, Barcelona (2010)Google Scholar
  10. 10.
    den Heijer, E., Eiben, A.: Comparing Aesthetic Measures for Evolutionary Art. In: Di Chio, C., Brabazon, A., Di Caro, G.A., Ebner, M., Farooq, M., Fink, A., Grahl, J., Greenfield, G., Machado, P., O’Neill, M., Tarantino, E., Urquhart, N. (eds.) EvoApplications 2010. LNCS, vol. 6025, pp. 311–320. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    den Heijer, E., Eiben, A.: Evolving art with scalable vector graphics. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO 2011, pp. 427–434. ACM (2011)Google Scholar
  12. 12.
    Machado, P., Cardoso, A.: All the truth about nevar. Applied Intelligence 16(2), 101–118 (2002)MATHCrossRefGoogle Scholar
  13. 13.
    Machado, P., Nunes, H., Romero, J.: Graph-Based Evolution of Visual Languages. In: Di Chio, C., Brabazon, A., Di Caro, G.A., Ebner, M., Farooq, M., Fink, A., Grahl, J., Greenfield, G., Machado, P., O’Neill, M., Tarantino, E., Urquhart, N. (eds.) EvoApplications 2010. LNCS, vol. 6025, pp. 271–280. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  14. 14.
    Matkovic, K., Neumann, L., Neumann, A., Psik, T., Purgathofer, W.: Global contrast factor-a new approach to image contrast. In: Neumann et al. [19], pp. 159–168Google Scholar
  15. 15.
    Neufeld, C., Ross, B., Ralph, W.: The evolution of artistic filters. In: Romero and Machado [19], pp. 335–356Google Scholar
  16. 16.
    Neumann, L., Sbert, M., Gooch, B., Purgathofer, W. (eds.): Computational Aesthetics 2005: Eurographics Workshop on Computational Aesthetics in Graphics, Visualization and Imaging 2005, Girona, Spain, May 18-20. Eurographics Association (2005)Google Scholar
  17. 17.
    O’Neill, M., Swafford, J.M., McDermott, J., Byrne, J., Brabazon, A., Shotton, E., McNally, C., Hemberg, M.: GECCO 2009, pp. 1035–1042. ACM (2009)Google Scholar
  18. 18.
    Perry, M.: Pulled: A Catalog of Screen Printing. Princeton Architectural Press (2011)Google Scholar
  19. 19.
    Romero, J., Machado, P. (eds.): The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music. Natural Computing Series. Springer, Heidelberg (2007)Google Scholar
  20. 20.
    Rooke, S.: Eons of genetically evolved algorithmic images. In: Bentley and Corne [3], pp. 339–365Google Scholar
  21. 21.
    Ross, B., Ralph, W., Zong, H.: Evolutionary image synthesis using a model of aesthetics. In: IEEE Congress on Evolutionary Computation, CEC 2006, pp. 1087–1094 (2006)Google Scholar
  22. 22.
    Schnier, T., Gero, J.S.: Learning genetic representations as alternative to handcoded shape grammars. In: Artificial Intelligence in Design (1996)Google Scholar
  23. 23.
    Selinger, P.: Potrace: a polygon-based tracing algorithm (2003), http://potrace.sourceforge.net/potrace.pdf
  24. 24.
    Sims, K.: Artificial evolution for computer graphics. In: SIGGRAPH 1991: Proceedings of the 18th Annual Conference on Computer Graphics and Interactive Techniques, vol. 25, pp. 319–328. ACM Press (July 1991)Google Scholar
  25. 25.
    (W3C), W.W.W.C.: Scalable vector graphics (svg), http://www.w3.org/Graphics/SVG/

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • E. den Heijer
    • 1
    • 2
  • A. E. Eiben
    • 2
  1. 1.Objectivation B.V.AmsterdamThe Netherlands
  2. 2.Vrije UniversiteitAmsterdamThe Netherlands

Personalised recommendations