Experiments in Computational Aesthetics

An Iterative Approach to Stylistic Change in Evolutionary Art
  • Penousal Machado
  • Juan Romero
  • Bill Manaris
Part of the Natural Computing Series book series (NCS)


A novel approach to the production of evolutionary art is presented. This approach is based on the promotion of an arms race between an adaptive classifier and an evolutionary computation system. An artificial neural network is trained to discriminate among images previously created by the evolutionary engine and famous paintings. Once training is over, evolutionary computation is used to generate images that the neural network classifies as paintings. The images created throughout the evolutionary run are added to the training set and the process is repeated. This iterative process leads to the refinement of the classifier and forces the evolutionary algorithm to explore new paths. The experimental results attained across iterations are presented and analyzed. Validation tests were conducted in order to assess the changes induced by the refinement of the classifier and to identify the types of images that are difficult to classify. Taken as a whole, the experimental results show the soundness and potential of the proposed approach.


Root Mean Square Error Fractal Dimension Average Root Mean Square Error Internal Image Genetic Programming System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Penousal Machado
    • 1
  • Juan Romero
    • 2
  • Bill Manaris
    • 3
  1. 1.CISUC Dept. of Informatics EngineeringUniversity of Coimbra3030 CoimbraPortugal
  2. 2.Faculty of Computer ScienceUniversity of CoruñaCP 15071Spain
  3. 3.Computer Science DeptCollege of CharlestonCharlestonUSA

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