Genetic Programming and Evolvable Machines

, Volume 14, Issue 3, pp 315–337 | Cite as

Learning aesthetic judgements in evolutionary art systems

  • Yang Li
  • Changjun Hu
  • Leandro L. Minku
  • Haolei Zuo


Learning aesthetic judgements is essential for reducing users’ fatigue in evolutionary art systems. Although judging beauty is a highly subjective task, we consider that certain features are important to please users. In this paper, we introduce an adaptive model to learn aesthetic judgements in the task of interactive evolutionary art. Following previous work, we explore a collection of aesthetic measurements based on aesthetic principles. We then reduce them to a relevant subset by feature selection, and build the model by learning the features extracted from previous interactions. To apply a more accurate model, multi-layer perceptron and C4.5 decision tree classifiers are compared. In order to test the efficacy of the approach, an evolutionary art system is built by adopting this model, which analyzes the user’s aesthetic judgements and approximates their implicit aesthetic intentions in the subsequent generations. We first tested these aesthetic measurements on different artworks from our selected artists. Then, a series of experiments were performed by a group of users to validate the adaptive learning model. The study reveals that different features are useful for identifying different patterns, but not all are relevant for the description of artists’ styles. Our results show that the use of the learning model in evolutionary art systems is sound and promising for predicting users’ preferences.


Evolutionary art Interactive evolutionary computation Image complexity Fractal compression 



The authors would like to thank the editor and the reviewers for their helpful insights on improving the manuscript and Penousal Machado and Juan Romero for their valuable comments. This work is supported by China Postdoctoral Science Foundation (No. 20110490296), "the Fundamental Research Funds for the Central Universities" (No. FRF-TP-12-079A), the 2012 Ladder Plan Project of Beijing Key Laboratory of Knowledge Engineering for Materials Science (No. Z121101002812005), National Program on Key Basic Research Project (973 Program) (No. 2013CB329606) and key Science–Technology Plan of the National "Twelfth Five-Year-Plan" of China under Grant (No. 2011BAK08B04).


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Yang Li
    • 1
    • 2
  • Changjun Hu
    • 1
  • Leandro L. Minku
    • 3
  • Haolei Zuo
    • 1
  1. 1.School of Computer and Communication EngineeringUniversity of Science and Technology BeijingBeijingChina
  2. 2.Beijing Key Laboratory of Knowledge Engineering for Materials ScienceBeijingChina
  3. 3.CERCIA, School of Computer ScienceThe University of BirminghamBirminghamUK

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