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Comparison between Genetic Algorithm and Genetic Programming Performance for Photomosaic Generation

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 5361)

Abstract

Photomosaics are a new form of art in which smaller digital images (known as tiles) are used to construct larger images. Photomosaic generation not only creates interest in the digital arts area but has also attracted interest in the area of evolutionary computing. The photomosaic generation process may be viewed as an arrangement optimisation problem, for a given set of tiles and suitable target to be solved using evolutionary computing. In this paper we assess two methods used to represent photomosaics, genetic algorithms (GAs) and genetic programming (GP), in terms of their flexibility and efficiency. Our results show that although both approaches sometimes use the same computational effort, GP is capable of generating finer photomosaics in fewer generations. In conclusion, we found that the GP representation is richer than the GA representation and offers additional flexibility for future photomosaics generation.

Keywords

  • Photomosaic
  • Genetic Programming (GP)
  • Genetic Algorithm (GA)

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Mat Sah, S.B., Ciesielski, V., D’Souza, D., Berry, M. (2008). Comparison between Genetic Algorithm and Genetic Programming Performance for Photomosaic Generation. In: , et al. Simulated Evolution and Learning. SEAL 2008. Lecture Notes in Computer Science, vol 5361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89694-4_27

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  • DOI: https://doi.org/10.1007/978-3-540-89694-4_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89693-7

  • Online ISBN: 978-3-540-89694-4

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