Learning aesthetic judgements in evolutionary art systems

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Li:2013:GPEM,
  author =       "Yang Li and Changjun Hu and Leandro L. Minku and 
                 Haolei Zuo",
  title =        "Learning aesthetic judgements in evolutionary art
                 systems",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2013",
  volume =       "14",
  number =       "3",
  pages =        "315--337",
  month =        sep,
  note =         "Special issue on biologically inspired music, sound,
                 art and design",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 art, Interactive evolutionary computation, IEC, Image
                 complexity, Fractal compression",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-013-9188-7",
  language =     "English",
  size =         "23 pages",
  abstract =     "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 analyses
                 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",
}

Genetic Programming entries for Yang Li Changjun Hu Leandro L Minku Haolei Zuo

Citations