Investigating Aesthetic Features to Model Human Preference in Evolutionary Art

  • Yang Li
  • Changjun Hu
  • Ming Chen
  • Jingyuan Hu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7247)

Abstract

In this paper we investigate aesthetic features in learning aesthetic judgments in an evolutionary art system. We evolve genetic art with our evolutionary art system, BioEAS, by using genetic programming and an aesthetic learning model. The model is built by learning both phenotype and genotype features, which we extracted from internal evolutionary images and external real world paintings, which could lead to more interesting paths. By learning aesthetic judgment and applying the knowledge to evolve aesthetical images, the model helps user to automate the process of evolutionary process. Several independent experimental results show that our system is efficient to reduce user fatigue in evolving art.

Keywords

Aesthetic learning evolutionary art interactive evolutionary computation computational aesthetics 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yang Li
    • 1
  • Changjun Hu
    • 1
  • Ming Chen
    • 2
  • Jingyuan Hu
    • 3
  1. 1.School of Computer and Communication EngineeringUniversity of Science and Technology BeijingBeijingChina
  2. 2.Tencent CompanyBeijingChina
  3. 3.Université de Technologie de TroyesFrance

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