Evolving Creative Portrait Painter Programs Using Darwinian Techniques with an Automatic Fitness Function
Inhaltsverzeichnis
Reference
Steve DiPaola: Evolving Creative Portrait Painter Programs Using Darwinian Techniques with an Automatic Fitness Function. In Proc: Electronic Visualisation and the Arts, 10 pages, London. July. 2005.
DOI
Abstract
We experiment with computer creativity by employing and modifying techniques from evolutionary computation to create a related family of abstract portrait painter programs. In evolutionary art, most systems evolve paintings by allowing the artist to selectively breed the artwork 'by hand' from a selection of the currently evolved population. Our system differs in that it uses an automatic 'creative fitness function' which allows the evolutionary process to run without stopping for 'creative human intervention'. A recent type of Genetic Programming (GP) is used called Cartesian GP, which has several features that allow our system to favour creative solutions over optimized solutions.
Extended Abstract
Bibtex
Used References
[1] BENTLEY, P and Corne, D (eds.): Creative Evolutionary Systems, Morgan Kaufmann, San Francisco (2002).
[2] ASHMORE, A and Miller, J: Evolutionary Art with Cartesian Genetic Programming, http://www.emoware.org/evolutionary_art.asp
[3] KOZA, J: Genetic Programming, MIT Press, London, (1993).
[4] MILLER, J and Thomson, P: Cartesian Genetic Programming, Proceedings of the 3rd European Conference on Genetic Programming, Edinburgh, (2000) 121-132.
[5] SIMS, K: Artificial Evolution for Computer Graphics. Computer Graphics, Vol. 25, (1991) 319-328.
[6] ROOKE, S: Eons of Genetically Evolved Algorithmic Images. In: Bentley P. J. and Corne D. (eds.): Creative Evolutionary systems, Morgan Kaufmann, (2002).
[7] MACHADO, P and Cardoso, A: NEvAr - The Assessment of an Evolutionary Art Tool. In: Wiggins, G. (Ed.). Proceedings of the AISB00 Symposium on Creative & Cultural Aspects and Applications of AI & Cognitive Science, UK, 2000.
[8] YU, T and Miller, J: Neutrality and the evolvability of Boolean function landscape. Proceedings of the Fourth European Conference on Genetic Programming, Springer-Verlag, Berlin (2001) 204-217.
[9] HOWE, N: Percentile Blobs for Image Similarity, IEEE Workshop on Content-Based Access of Image and Video Databases, 1998.
Links
Full Text
http://www.dipaola.org/evolve/darwin/dipaola_eva05.doc