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Evaluating an evolutionary method of design style imitation

Published online by Cambridge University Press:  11 May 2010

Andrés Gómez de Silva Garza
Affiliation:
Instituto Tecnológico Autónomo de México, Mexico City, Mexico
Arám Zamora Lores
Affiliation:
Instituto Tecnológico Autónomo de México, Mexico City, Mexico

Abstract

We propose a computational method for producing novel constructs that fall within an existing design or artistic style. The method is based on evolutionary algorithms, and we discuss related knowledge representation issues. We then present an implementation of this method that we used in order to imitate the style of the Dutch painter Mondrian. Finally, we explain and give the results of a cognitive experiment designed to determine the effectiveness of the method, and provide a discussion of these results.

Type
Articles
Copyright
Copyright © Cambridge University Press 2010

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References

REFERENCES

Bax, M. (2001). Complete Mondrian. Aldershot: Lund Humphries/Ashgate Publishing.Google Scholar
Bentley, P., Ed. (1999). Evolutionary Design by Computers. San Francisco, CA: Morgan Kaufmann.Google Scholar
Bentley, P., & Corne, D.W., Eds. (2002). Creative Evolutionary Systems. San Francisco, CA: Morgan Kaufmann.Google Scholar
Boden, M. (2003). The Creative Mind: Myths and Mechanisms. London: Routledge.Google Scholar
Cha, M.-Y., & Gero, J.S. (1999). Style learning: inductive generalisation of architectural shape patterns. In Architectural Computing from Turing to 2000 (eCAADe) (Brown, A., Knight, M., & Berridge, P., Eds.), pp. 629644. University of Liverpool.Google Scholar
Deicher, S. (1999). Mondrian. Cologne: Benedikt Taschen Verlag.Google Scholar
Ding, L., & Gero, J.S. (2001). The emergence of the representation of style in design. Environment and Planning B: Planning and Design 28 (5), 707731.Google Scholar
Eiben, A.E., Nabuurs, R., & Booij, I. (2002). The Escher Evolver: evolution to the people. In Creative Evolutionary Systems (Bentley, P., & Corne, D.W., Eds.), pp. 425439. San Francisco, CA: Morgan Kaufmann.Google Scholar
Gero, J.S. (1990). Design prototypes: a knowledge representation schema for design. AI Magazine 11 (4), 2636.Google Scholar
Gero, J.S., & Jupp, J.R. (2003). Feature based qualitative representation of architectural plans. Proc. Computer-Aided Architectural Design Research in Asia Conf. (CAADRIA-03).Google Scholar
Gómez de Silva Garza, A., & Zamora Lores, A. (2004). A cognitive evaluation of a computer system for generating Mondrian-like artwork. In Design Computing and Cognition DCC'04 (Gero, J.S., Ed.), pp. 7996. Dordrecht: Kluwer Academic.Google Scholar
Gómez de Silva Garza, A., & Zamora Lores, A. (2005). Case-based art. In Case-Based Reasoning Research and Development (ICCBR'05) (Muñoz-Ávila, H., & Ricci, F., Eds.), pp. 237251. Berlin: Springer–Verlag.Google Scholar
Hancock, P.J.B., & Frowd, C.D. (2002). Evolutionary generation of faces. In Creative Evolutionary Systems (Bentley, P., & Corne, D.W., Eds.), pp. 409424. San Francisco, CA: Morgan Kaufmann.Google Scholar
Kane, C., & Schoenauer, M. (1996). Topological optimum design using genetic algorithms. Control and Cybernetics 25 (5), 125.Google Scholar
Koza, J. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: MIT Press.Google Scholar
McCorduck, P. (1991). Aaron's Code: Meta-Art, Artificial Intelligence, and the Work of Harold Cohen. New York: W.H. Freeman.Google Scholar
Mitchell, M. (1998). An Introduction to Genetic Algorithms (Complex Adaptive Systems Series). Cambridge, MA: MIT Press.Google Scholar
Pagliarini, L., & Lund, H.H. (2002). Art, robots, and evolution as a tool for creativity. In Creative Evolutionary Systems (Bentley, P., & Corne, D.W., Eds.), pp. 367385. San Francisco, CA: Morgan Kaufmann.Google Scholar
Rosch, E. (1988). Principles of categorization. In Readings in Cognitive Science (Collins, A.C., & Smith, E.E., Eds.), pp. 312322. San Mateo, CA: Morgan Kaufmann.Google Scholar
Rooke, S. (2002). Eons of genetically evolved algorithmic images. In Creative Evolutionary Systems (Bentley, P., & Corne, D.W., Eds.), pp. 339365. San Francisco, CA: Morgan Kaufmann.Google Scholar
Rowbottom, A. (1999). Evolutionary art and form. In Evolutionary Design by Computers (Bentley, P., Ed.), pp. 261277. San Francisco, CA: Morgan Kaufmann.Google Scholar
Schnier, T., & Gero, J. (1997). Dominant and recessive genes in evolutionary systems applied to spatial reasoning. In Advanced Topics in Artificial Intelligence: 10th Joint Australian Conf. Artificial Intelligence (Sattar, A., Ed.), pp. 127136. Heidelberg: Springer–Verlag.Google Scholar
Schnier, T., & Gero, J.S. (1998). From Mondrian to Frank Lloyd Wright: transforming evolving representations. In Adaptive Computing in Design and Manufacture III (Parmee, I., Ed.), pp. 207219. London: Springer–Verlag.Google Scholar
Schreiner, D., Woo, M., Neider, J., & Davis, T. (2007). OpenGL Programming Guide: The Official Guide to Learning OpenGL. Reading, MA: Addison–Wesley.Google Scholar
Todd, S., & Latham, W. (1999). The mutation and growth of art by computers. In Evolutionary Design by Computers (Bentley, P., Ed.), pp. 221250. San Francisco, CA: Morgan Kaufmann.Google Scholar
Welsh, R.P., & Joosten, J.M. (1998). Piet Mondrian Catalogue Raisonné (Vols. 1 & 2). New York: Harry N. Abrams.Google Scholar
Witbrock, T., & Neil-Reilly, S. (1999). Evolving genetic art. In Evolutionary Design by Computers (Bentley, P., Ed.), pp. 251259. San Francisco, CA: Morgan Kaufmann.Google Scholar
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