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International Conference on Neural Information Processing

ICONIP 2016: Neural Information Processing pp 261–268Cite as

The Evolutionary Process of Image Transition in Conjunction with Box and Strip Mutation

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 9949)

Abstract

Evolutionary algorithms have been used in many ways to generate digital art. We study how evolutionary processes are used for evolutionary art and present a new approach to the transition of images. Our main idea is to define evolutionary processes for digital image transition, combining different variants of mutation and evolutionary mechanisms. We introduce box and strip mutation operators which are specifically designed for image transition. Our experimental results show that the process of an evolutionary algorithm in combination with these mutation operators can be used as a valuable way to produce unique generative art.

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Notes

  1. 1.

    Images and videos are available at https://evolutionary-art.blogspot.com.

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Author information

Authors and Affiliations

  1. Optimisation and Logistics, School of Computer Science, The University of Adelaide, Adelaide, Australia

    Aneta Neumann, Bradley Alexander & Frank Neumann

Authors
  1. Aneta Neumann
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  2. Bradley Alexander
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  3. Frank Neumann
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Corresponding author

Correspondence to Aneta Neumann .

Editor information

Editors and Affiliations

  1. The University of Tokyo , Tokyo, Japan

    Akira Hirose

  2. Kobe University , Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology , Ikoma, Japan

    Kazushi Ikeda

  5. Kyungpook National University , Daegu, Korea (Republic of)

    Minho Lee

  6. Chinese Academy of Sciences , Beijing, China

    Derong Liu

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Neumann, A., Alexander, B., Neumann, F. (2016). The Evolutionary Process of Image Transition in Conjunction with Box and Strip Mutation. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9949. Springer, Cham. https://doi.org/10.1007/978-3-319-46675-0_29

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  • DOI: https://doi.org/10.1007/978-3-319-46675-0_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46674-3

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