Evolving Portrait Painter Programs using Genetic Programming to Explore Computer Creativity

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Reference

Steve DiPaola: Evolving Portrait Painter Programs using Genetic Programming to Explore Computer Creativity. In Proc: iDMAa Conference (Intl Digital Media and Arts Association), 7 pages, 2006.

DOI

Abstract

Creative systems as opposed to standard evolutionary systems favor exploration over optimization, finding innovative or novel solutions over a preconceived notion of a specific optimal solution. The best creative evolutionary systems only provide tools, allowing the evolutionary process to discover novelty and innovation on its own. We experiment with computer creativity by employing and modifying techniques from evolutionary computation to create a related family of abstract portraits. A new type of Genetic Programming (GP) system is used called Cartesian GP, which uses typical GP Darwinian evolutionary techniques (crossover, mutation, and survival), but has several features that allow the GP system to favor creative solutions over optimized solutions including accommodating for genetic drift where different genotypes map to the same phenotype, visual mapping modules and a knowledge of a painterly color space. This work with its specific goal of evolving portrait painter programs to create a portrait 'sparked' by the famous portrait of Darwin, speaks to the evolutionary processes as well as creativity, as seen by the early results where the evolving programs use recurring, emergent and merged creative strategies to become good abstract portraitists.

Extended Abstract

Bibtex

Used References

[1] Ashmore, L. and Miller, J. 2004. Evolutionary Art with Cartesian Genetic Programming. Technical Online Report. http://www.emoware.org/evolutionary_art.asp.

[2] Bentley, P., and Corne, D. eds. 2002. Creative Evolutionary Systems, San Francisco, CA.: Morgan Kaufmann.

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[7] Miller, J. and Thomson, P. 2000. Cartesian Genetic Programming. Proceedings of the 3rd European Conference on Genetic Programming, 121-132. Edinburgh, UK.

[8] Montes, H. and Wyatt, J. 2003. Cartesian Genetic Programming for Image Processing Tasks. Proceedings of the International Conference of Neural Networks and Computational Intelligence, 185-190. Cancun, Mexico.

[9] Rooke, S. 2002. Eons of Genetically Evolved Algorithmic Images. In: Bentley P. J. and Corne D. (eds.): Creative Evolutionary systems, Morgan Kaufmann.

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[11] Yu, T. and Miller, J. 2001. Neutrality and the Evolvability of Boolean function landscape. Proceedings of the Fourth European Conference on Genetic Programming, 204-217. Berlin Springer- Verlag.


Links

Full Text

http://www.units.muohio.edu/codeconference/papers/papers/idmapaper1.pdf

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Sonstige Links

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.152.8968