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Comparing Aesthetic Measures for Evolutionary Art

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Applications of Evolutionary Computation (EvoApplications 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6025))

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Abstract

In this paper we investigate and compare four aesthetic measures within the context of evolutionary art. We evolve visual art with an unsupervised evolutionary art system using genetic programming and an aesthetic measure as the fitness function. We perform multiple experiments with different aesthetic measures and examine their influence on the evolved images. To this end we store the 5 fittest individuals of each run and hand-pick the best 9 images after finishing the whole series. This way we create a portfolio of evolved art for each aesthetic measure for visual inspection. Additionally, we perform a cross-evaluation by calculating the aesthetic value of images evolved by measure i according to measure j. This way we investigate the flexiblity of each aesthetic measure (i.e., whether the aesthetic measure appreciates different types of images). The results show that aesthetic measures have a rather clear ”style” and that these styles can be very different. Furthermore we find that some aesthetic measures show very little flexibility and appreciate only a limited set of images.

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Author information

Authors and Affiliations

  1. Objectivation B.V., Amsterdam, The Netherlands

    E. den Heijer

  2. Vrije Universiteit, Amsterdam, The Netherlands

    E. den Heijer & A. E. Eiben

Authors
  1. E. den Heijer
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  2. A. E. Eiben
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Statistics, University of Strathclyde, 16 Richmond Street, G1 1XQ, Glasgow, UK

    Cecilia Di Chio

  2. School of Business, University College Dublin, Belfield, Dublin 4, UK

    Anthony Brabazon

  3. ’Dalla Molle’ Institute for Artificial Intelligence (IDSIA), Galleria 2, 6928, Manno-Lugano, Switzerland

    Gianni A. Di Caro

  4. Wilhelm Schickard-Institut für Informatik, Universität Tübingen, Sand 1, 72076, Tübingen, Germany

    Marc Ebner

  5. National University of Computer and Emerging Sciences (NUCES), A.K. Brohi Road, H-11/4, Islamabad, Pakistan

    Muddassar Farooq

  6. Fakultät für WiSo, HelmutSchmidt-Universität, Holstenhofweg 85, 22043, Hamburg, Germany

    Andreas Fink

  7. Department of Information Systems, Johannes Gutenberg-Universität, 55099, Mainz, Germany

    Jörn Grahl

  8. Mathematics and Computer Science Depatment, University of Richmond, 23173, Richmond, VA, USA

    Gary Greenfield

  9. Department of Informatics Engineering, Polo II - Pinhal de Marrocos, University of Coimbra, 3030 - 290, Coimbra, Portugal

    Penousal Machado

  10. School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, UK

    Michael O’Neill

  11. Istituto di Calcolo e Reti ad Alte Prestazioni (ICAR), Consiglio Nazionale delle Ricerche (CNR), Via P. Castellino 111, 80131, Naples, Italy

    Ernesto Tarantino

  12. School of Computing, Edinburgh Napier University, 10 Clinton Road, EH10 5DT, Edinburgh, UK

    Neil Urquhart

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den Heijer, E., Eiben, A.E. (2010). Comparing Aesthetic Measures for Evolutionary Art. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2010. Lecture Notes in Computer Science, vol 6025. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12242-2_32

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  • DOI: https://doi.org/10.1007/978-3-642-12242-2_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12241-5

  • Online ISBN: 978-3-642-12242-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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