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.
Images and videos are available at https://evolutionary-art.blogspot.com.
References
Bentley, P., Corne, D.: Creative Evolutionary Systems. Evolutionary Computation Series. Morgan Kaufmann, San Francisco (2002)
McCormack, J., d’Inverno, M. (eds.): Computers and Creativity. Springer, Heidelberg (2012)
Dawkins, R.: The Blind Watchmaker: Why the Evidence of Evolution Reveals a Universe Without Design. National Bestseller. Science. Norton, New York (1986)
Smith, J.: Evolution and the Theory of Games. Cambridge University Press, Cambridge (1982)
Sims, K.: Artificial evolution for computer graphics. In: Thomas, J.J. (eds.) Proceedings of the 18th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1991, pp. 319–328. ACM (1991)
Todd, S., Latham, W.: Evolutionary Art and Computers. Academic Press Inc., Orlando (1994)
Unemi, T.: Sbart4 for an automatic evolutionary art. In: Proceedings of the IEEECongress on Evolutionary Computation, CEC 2012, Brisbane, Australia, June 10-15, 2012, pp. 1–8. IEEE (2012)
Hart, D.: Toward greater artistic control for interactive evolution of images and animation. In: Finnegan, J.W., Pfister, H. (eds.) 33 International Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2006. ACM 2(2006)
Champandard, A.J.: Semantic style transfer and turning two-bit doodles into fine artworks. CoRR abs/1603.01768 (2016)
Li, C., Wand, M.: Combining Markov random fields and convolutional neural networks for image synthesis. CoRR abs/1601.04589 (2016)
Droste, S., Jansen, T., Wegener, I.: On the analysis of the (1 + 1) evolutionary algorithm. Theor. Comput. Sci. 276(1–2), 51–81 (2002)
Jansen, T., Sudholt, D.: Analysis of an asymmetric mutation operator. Evol. Comput. 18(1), 1–26 (2010)
Neumann, F., Wegener, I.: Randomized local search, evolutionary algorithms, and the minimum spanning tree problem. Theor. Comput. Sci. 378(1), 32–40 (2007)
Mitzenmacher, M., Upfal, E.: Probability and Computing: Randomized Algorithms and Probabilistic Analysis. Cambridge University Press, New York (2005)
He, J., Yao, X.: Drift analysis and average time complexity of evolutionary algorithms. Artif. Intell. 127(1), 57–85 (2001)
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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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