Aesthetic Learning in an Interactive Evolutionary Art System

  • Yang Li
  • Chang-Jun Hu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6025)


Learning aesthetic judgements is essential for reducing the users’ fatigue in evolutionary art system. Although judging beauty is a highly subjective task, we consider that certain features are important to please users. In this paper, the aesthetic preferences are explored by learning the features, which we extracted from the images in the interactive generations. In addition to color ingredients, image complexity and image order are considered highly relevant to aesthetic measurement. We interpret these two features from the information theory and fractal compression perspective. Our experimental results suggest that these features play important roles in aesthetic judgements. Our findings also show that our evolutionary art system is efficient at predicting user’s preference.


Evolutionary art interactive evolutionary computation image complexity fractal compression 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Dawkins, R.: The Blind Watchmaker. Harlow Longman (1986)Google Scholar
  2. 2.
    Sims, K.: Artificial Evolution For Computer Graphics. In: Proc. of the 18th Annual Conference on Computer Graphics and Interactive Techniques SIGGRAPH 1991, pp. 319–328. ACM Press, New York (1991)CrossRefGoogle Scholar
  3. 3.
    Lutton, E.: Evolution of Fractal shapes for artists and designers. International Journal on Artificial Intelligence Tools 15(4), 651–672 (2006)CrossRefGoogle Scholar
  4. 4.
    Machado, P., Cardoso, A.: All the Truth About NEvAr. In: Corne, D.P., Bently (eds.) Applied Intelligence, Special issue on Creative Systems, vol. 16(2), pp. 101–119. Kluwer Academic Publishers, Dordrecht (2002)Google Scholar
  5. 5.
    Wang, S.F., Wang, S., Takagi, H.: User Fatigue Reduction by an Absolute Rating Data-trained Predictor in IEC. In: Proc. IEEE Congress on Evolutionary Computation, pp. 2195–2200. IEEE Press, New York (2006)CrossRefGoogle Scholar
  6. 6.
    Takagi, H.: Interactive Evolutionary Computation. In: Proc. of the 5th International Conference on Soft Computing and Information / Intelligent Systems, Iizuka, Japan, pp. 41–50 (1998)Google Scholar
  7. 7.
    Birkhoff, G.D.: Aesthetic Measure. Harvard University Press, Cambridge (1933)zbMATHGoogle Scholar
  8. 8.
    Rigau, J., Feixas, M., Sbert, M.: Informational Aesthetics Measures. In: Proc. IEEE Computer society, pp. 24–34 (2008)Google Scholar
  9. 9.
    Schmidhuber, J.: Low-complexity art. Leonardo. Journal of the International Society for the Arts, Sciences, and Technology 30(2), 97–103 (1997)Google Scholar
  10. 10.
    Scha, R., Bod, R.: Informatie en Informatiebeleid 11(1), 54–63 (1993), English translation
  11. 11.
    Machado, P., Romero, J., Manaris, B.: Experiments in Computational Aesthetics: An Iterative Approach to Stylistic Change in Evolutionary Art. In: The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music, pp. 381–415. Springer, Heidelberg (2008)Google Scholar
  12. 12.
    Witten, H.I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann Publishers, San Francisco (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Yang Li
    • 1
  • Chang-Jun Hu
    • 1
  1. 1.School of Information EngineeringUniversity of Science and Technology BeijingBeijingChina

Personalised recommendations