An Empirical Exploration of a Definition of Creative Novelty for Generative Art

  • Taras Kowaliw
  • Alan Dorin
  • Jon McCormack
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5865)


We explore a new definition of creativity — one which emphasizes the statistical capacity of a system to generate previously unseen patterns — and discuss motivations for this perspective in the context of machine learning. We show the definition to be computationally tractable, and apply it to the domain of generative art, utilizing a collection of features drawn from image processing. We next utilize our model of creativity in an interactive evolutionary art task, that of generating biomorphs. An individual biomorph is considered a potentially creative system by considering its capacity to generate novel children. We consider the creativity of biomorphs discovered via interactive evolution, via our creativity measure, and as a control, via totally random generation. It is shown that both the former methods find individuals deemed creative by our measure; Further, we argue that several of the discovered “creative” individuals are novel in a human-understandable way. We conclude that our creativity measure has the capacity to aid in user-guided evolutionary tasks.


Feature Space Empirical Exploration Phenotypic Space Creativity Lite Creative System 
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  1. 1.
    Dawkins, R.: The Blind Watchmaker. Longman Scientific & Technical (1986)Google Scholar
  2. 2.
    Dorin, A., Korb, K.: Improbable creativity. In: McCormack, J., Boden, M., d’Inverno, M. (eds.) Proceedings of the Dagstuhl International Seminar on Computational Creativity. Springer, Heidelberg (2009)Google Scholar
  3. 3.
    Haralick, R.: Statistical and structural approaches to texture. Proceedings of the IEEE 67, 786–804 (1979)CrossRefGoogle Scholar
  4. 4.
    Howarth, P., Rüger, S.: Evaluation of texture features for content-based image retrieval. In: Enser, P.G.B., et al. (eds.) CIVR 2004. LNCS, vol. 3115, pp. 326–334. Springer, Heidelberg (2004)Google Scholar
  5. 5.
    Kowaliw, T., Banzhaf, W., Kharma, N., Harding, S.: Evolving novel image features using genetic programming-based image transforms. In: IEEE CEC 2009 (2009)Google Scholar
  6. 6.
    Machado, P., Romero, J., Manaris, B.: Experiments in computational aesthetics: An iterative approach to stylistic change in evolutionary art. In: Romero, J., Machado, P. (eds.) The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music, pp. 381–415 (2008)Google Scholar
  7. 7.
    McCormack, J.: Facing the future: Evolutionary possibilities for human-machine creativity. In: Romero, J., Machado, P. (eds.) The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music, pp. 417–453 (2008)Google Scholar
  8. 8.
    Vailaya, A., Figueiredo, M.A.T., Jain, A.K., Zhang, H.-J.: Image classification for content-based indexing. IEEE Transactions on Image Processing 10(1), 117–130 (2001)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Taras Kowaliw
    • 1
  • Alan Dorin
    • 1
  • Jon McCormack
    • 1
  1. 1.Centre for Electronic Media Art, Faculty of Information TechnologyMonash UniversityClaytonAustralia

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