Yang Li, Chang-Jun Hu, Jing-Qin Pang


An aesthetic learning model is proposed that applies evolutionary algorithm to generate art. The model is evaluated using an evolutionary art system by human subjects. The advantages of the model is that it helps user to automate the process of image evolution by learning the user’s preferences and applying the knowledge to evolve aesthetical images. This paper implements four categories of aesthetic metrics to establish user’s criteria. In addition to evolutionary images, external artworks are also included to guide evolutionary process towards more interesting paths. Then we described an evolutionary art system which adopted the aesthetic model in detail. Last, we evaluate the aesthetic learning model in several independent experiments to show the efficiency at predicting user’s preferences.


  1. Bachelier, G. (2008). The Art of Artificial Evolution:A Handbook on Evolutionary Art and Music, chapter Embedding of Pixel-Based Evolutionary Algorithms in My Global Art Process, pages 249-268. Springer Berlin Heidelberg New York.
  2. Baluja, S., Pomerlau, D., and Todd, J. (1994). Towards automated artificial evolution for computer-generated images. Connection Science, 6(2):325-354.
  3. Bense, M. (1969). Einfúhrung in die informationstheoretische Asthetik, chapter Graundlegung und Anwendung in der Texttheorie (Introduction to the Informationtheoretical Aesthetics. Foundation and Application to the Text Theory). Rowohlt Taschenbuch Verlag.
  4. Bentley, P. (1999). Aspects of evolutionary design by computers. In Proc. of Advances in Soft ComputingEngineering Design and Manufacturing, pages 99- 118. London.
  5. Birkhoff, G. D. (1933). Aesthetic Measure. Harvard University Press.
  6. Dawkins, R. (1986). The Blind Watchmaker. Harlow Longman.
  7. Ekart, A., Sharma, D., and Chalakov, S. (2011). Modelling human preference in evolutionary art. In Evo Applications 2011, Part II, LNCS 6625, pages 303-312.
  8. Greenfield, G. (2005). On the origins of the term computational aesthetics. pages 9-12, Aire-la-Ville, Switzerland.
  9. Hoenig, F. (2005). Defining computational aesthetics. In Neumann L., Sbert M., Gooch B., Purgathofer W., (Eds.), Eurographics Association.
  10. Latham, W. and Todd, S. (1992). Evolutionary Art and Computers. Academic Press, Winchester, UK.
  11. Li, Y. and Hu, C. J. (2010). Aesthetic learning in an interactive evolutionary art system. In Evo Applications 2010, Part II, LNCS 6025, pages 301-310.
  12. Li, Y., Hu, C. J., and Yao, X. (2009). Innovative batik design with an interactive evolutionary art system. Journal of Computer Science and Technology, 24(6):1035-1047.
  13. Lutton, E. (2006). Evolution of fractal shapes for artists and designers. International Journal on Artificial Intelligence Tools, 15(4):651-672.
  14. Machado, P. and Cardoso, A. (1998). Computing aesthetics. In Proceedings of XIVth Brazilian Symposium on Artificial Intelligence(SBIA 7898), LNCS, pages 219-229, Porto Alegre, Brazil.
  15. Machado, P. and Cardoso, A. (2002). All the truth about NEvAr. Applied Intelligence, Special Issue on Creative Systems, 16(2):101-119.
  16. Machado, P., Romero, J., and Manaris, B. (2008). In The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music, chapter P. Machado:Experiments in Computational Aesthetics: An Iterative Approach to Stylistic Change in Evolutionary Art, pages 381- 415. SPRINGER.
  17. Manaris, B., Romero, J., Machado, P., Krehbiel, D., Hirzel, T., Pharr, W., , and Davis, R. (2005). Zipf's law, music classification and aesthetics. Computer Music Journal, 29(1):55-69.
  18. Manaris, B., Roos, P., Machado, P., Krehbiel, D., Pellicoro, L., and Romero, J. (2007). A corpus-based bybrid approach to music analysis and composition. In Proc. of the 22nd Conference on Artificial Intelligence (AAAI 07).
  19. Papadoulos, G. and Wiggins, G. (1999). Ai methods for algorithmic composition: A survey, a critical view and future prospects. In Proc. of AISB'99 Symposium on Musical Creativity, pages 110-117, Edinburgh, UK.
  20. Poli, R. and Cagnoni, S. (1991). Genetic programming with user-driven selection: Experiments on the evolutiona of algorithms for image enhancement. In Proc. 2nd Annual Conf. on Genetic Programming, pages 269- 277. Morgan Kaufmann.
  21. Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers.
  22. Ralph, W. (2006). Painting the bell curve: The occurrence of the normal distribution in fine art. In preparation.
  23. Rigau, J., Feixas, M., and Sbert, M. (2008). Informational aesthetics measures. Computer Graphics and Applications, 28(2):24-34.
  24. Rooke, S. (2002). Eons of Genetically Evolved Algorithmic Images, pages 330-365. Morgan Kaufmann.
  25. Ross, B. and Zhu, H. (2004). Procedural texture evolution using multiobjective optimization. New Generation Computing, 22(3):271-293.
  26. Ross, B. J., Ralph, W., and Zong, H. (2006). Evolutionary image synthesis using a model of aethetics. In Proc. 2006 IEEE Congress on Evolutionary Computation, pages 1087-1094, Vancouver, BC, Canada.
  27. Schmidhuber, J. (1997). Low-complesity art. Leonardo. Journal of the International Society for the Arts, Sciences, and Technology, 30(2):93-103.
  28. Shannon, C. (1951). Prediction and entropy of printed english. Bell System Technical Journal, 30:50-64.
  29. Sims, K. (1991). Artificial evolution for computer graphics. In Proc. of the 18th Annual Conference on Computer Graphics and Interactive Techniques SIGGRAPH 7891, pages 319-328. New York, NY: ACM Press.
  30. Takagi, H. (1998). Interactive evolutionary computation. In Proc. of the 5th International Conference on Soft Computing and Information Intelligent Systems, pages 41-50, Iizuka, Japan.
  31. Todd, P. M. and Werner, G. (1998). Frankensteinian Methods for Evolutionary Music Composition, pages 313- 339. MIT Press/Bradford Books.
  32. Ventrella, J. (2008). Evolving the mandelbrot set to imitate figurative art. Innovations in Evolutionary Design.
  33. Wannarumon, S., Bohez, E., and Annanon, K. (2008). Aesthetic evolutionary algorithm for fractal-based usercentered jewelry design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing (AI EDAM), 22(1):19-39.
  34. Witten, I. and Frank, E. (2000). Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann Publishers.

Paper Citation

in Harvard Style

Li Y., Hu C. and Pang J. (2011). USING AESTHETIC LEARNING MODEL TO CREATE ART . In Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2011) ISBN 978-989-8425-83-6, pages 174-183. DOI: 10.5220/0003670001740183

in Bibtex Style

author={Yang Li and Chang-Jun Hu and Jing-Qin Pang},
booktitle={Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2011)},

in EndNote Style

JO - Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2011)
SN - 978-989-8425-83-6
AU - Li Y.
AU - Hu C.
AU - Pang J.
PY - 2011
SP - 174
EP - 183
DO - 10.5220/0003670001740183