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Evolving Art Using Multiple Aesthetic Measures

  • E. den Heijer
  • A. E. Eiben
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6625)

Abstract

In this paper we investigate the applicability of Multi-Objective Optimization (MOO) in Evolutionary Art. We evolve images using an unsupervised evolutionary algorithm and we use two aesthetic measures as fitness functions concurrently. We use three different pairs from a set of three aesthetic measures and we compare the output of each pair to the output of other pairs, and to the output of experiments with a single aesthetic measure (non-MOO). We investigate 1) whether properties of aesthetic measures can be combined using MOO and 2) whether the use of MOO in evolutionary art results in different images, or perhaps “better” images. All images in this paper can be viewed in colour at http://www.few.vu.nl/~eelco/

Keywords

Pareto Front Multiobjective Optimization Optimal Pareto Front Color Transition Bell Curve 
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 2011

Authors and Affiliations

  • E. den Heijer
    • 1
    • 2
  • A. E. Eiben
    • 2
  1. 1.Objectivation B.V.AmsterdamThe Netherlands
  2. 2.Vrije UniversiteitAmsterdamThe Netherlands

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