Comparison between genetic algorithm and genetic programming performance for photomosaic generation

Comparison between genetic algorithm and genetic programming performance for photomosaic generation

Mat Sah, S, Ciesielski, V, D'Souza, D and Berry, M 2008, 'Comparison between genetic algorithm and genetic programming performance for photomosaic generation', in X. Li et al (ed.) 7th International Conference. Simulated Evolution and Learning, SEAL 2008, Melbourne, Australia, December 2008, Proceedings, Berlin, Germany, 7-10 December 2008, pp. 259-268.

Document type:	Conference Paper
Collection:	Conference Papers

Title	Comparison between genetic algorithm and genetic programming performance for photomosaic generation
Author(s)	Mat Sah, S Ciesielski, V D'Souza, D Berry, M
Year	2008
Conference name	International Conference on Simulated Evolution and Learning (SEAL 2008)
Conference location	Berlin, Germany
Conference dates	7-10 December 2008
Proceedings title	7th International Conference. Simulated Evolution and Learning, SEAL 2008, Melbourne, Australia, December 2008, Proceedings
Editor(s)	X. Li et al
Publisher	Springer
Place of publication	Berlin, Germany
Start page	259
End page	268
Total pages	10
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.
Subjects	Artificial Intelligence and Image Processing not elsewhere classified
Keyword(s)	Photomosaic Genetic Programming (GP) Genetic Algorithm (GA)
Copyright notice	© Springer-Verlag Berlin Heidelberg 2008
ISBN	3540896937

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