Experiments in Computational Aesthetics

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Machado, Penousal; Romero, Juan; Manaris, Bill: Experiments in Computational Aesthetics. In: Romero, Juan; Machado, Penousal: The Art of Artificial Evolution. Springer, Berlin, 2007, S. 381-415.




A novel approach to the production of evolutionary art is presented. This approach is based on the promotion of an arms race between an adaptive classifier and an evolutionary computation system. An artificial neural network is trained to discriminate among images previously created by the evolutionary engine and famous paintings. Once training is over, evolutionary computation is used to generate images that the neural network classifies as paintings. The images created throughout the evolutionary run are added to the training set and the process is repeated. This iterative process leads to the refinement of the classifier and forces the evolutionary algorithm to explore new paths. The experimental results attained across iterations are presented and analyzed. Validation tests were conducted in order to assess the changes induced by the refinement of the classifier and to identify the types of images that are difficult to classify. Taken as a whole, the experimental results show the soundness and potential of the proposed approach.

Extended Abstract


Used References

Machado, P., Cardoso, A. (1997). Model proposal for a constructed artist. In Callaos, N., Khoong, C., Cohen, E., eds.: First World Multiconference on Systemics, Cybernetics and Informatics, SCI97/ISAS97. Caracas, Venezuela, 521–528

Romero, J. (2002). Metodología Evolutiva para la Construcción de Modelos Cognitivos Complejos. Exploración de la Creatividad Artificial en Composición Musical. PhD thesis. University of Corunha. Corunha, Spain (in Spanish)

Romero, J., Machado, P., Santos, A., Cardoso, A. (2003). On the development of critics in evolutionary computation artists. In Günther, R., et al., eds.: Applications of Evolutionary Computing, EvoWorkshops 2003: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, EvoSTOC. Vol. 2611 of LNCS. Essex, UK. Springer

Manaris, B., Purewal, T., McCormick, C. (2002). Progress towards recognizing and classifying beautiful music with computers — MIDI-encoded music and the Zipf–Mandelbrot law. In: Proceedings of the IEEE SoutheastCon. Columbia, SC

Manaris, B., Romero, J., Machado, P., Krehbiel, D., Hirzel, T., Pharr, W., Davis, R. (2005). Zipf’s law, music classification and aesthetics. Computer Music Journal, 29(1): 55–69

Manaris, B., Roos, P., Machado, P., Krehbiel, D., Pellicoro, L., Romero, J. (2007). A corpus-based hybrid approach to music analysis and composition. In: Proceedings of the 22nd Conference on Artificial Intelligence (AAAI 07). Vancouver, BC

Machado, P., Romero, J., Santos, A., Cardoso, A., Manaris, B. (2004). Adaptive critics for evolutionary artists. In Günther, R., et al., eds.: Applications of Evolutionary Computing, EvoWorkshops 2004: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, EvoSTOC. Vol. 3005 of LNCS. Coimbra, Portugal. Springer, 435–444

Machado, P., Romero, J., Cardoso, A., Santos, A. (2005). Partially interactive evolutionary artists. New Generation Computing – Special Issue on Interactive Evolutionary Computation, 23(42): 143–155

Machado, P. (2007). Inteligência Artificial e Arte. PhD thesis. University of Coimbra. Coimbra, Portugal (in Portuguese)

Machado, P., Cardoso, A. (1998). Computing aesthetics. In Oliveira, F., ed.: Proceedings of the XIVth Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence. Vol. 1515 of LNCS. Porto Alegre, Brazil. Springer, 219–229

Taylor, R.P., Micolich, A.P., Jonas, D. (1999). Fractal analysis of Pollock’s drip paintings. Nature, 399: 422

Saunders, R. (2001). Curious Design Agents and Artificial Creativity — A Synthetic Approach to the Study of Creative Behaviour. PhD thesis. University of Sydney, Department of Architectural and Design Science Faculty of Architecture. Sydney, Australia

Manaris, B.Z., Vaughan, D., Wagner, C., Romero, J., Davis, R.B. (2003). Evolutionary music and the Zipf–Mandelbrot law: Developing fitness functions for pleasant music. In Günther, R., et al., eds.: Applications of Evolutionary Computing, EvoWorkshop 2003: EvoBIO, EvoCOP, EvoIASP, EvoMUSART, EvoROB, and EvoSTIM. Vol. 2611 of LNCS. Springer, 522–534

Machado, P., Romero, J., Manaris, B., Santos, A., Cardoso, A. (2003). Power to the critics — A framework for the development of artificial art critics. In: IJCAI 2003 Workshop on Creative Systems. Acapulco, Mexico

Manaris, B., Machado, P., McCauley, C., Romero, J., Krehbiel, D. (2005). Developing fitness functions for pleasant music: Zipf’s law and interactive evolution systems. In Rothlauf, F., et al., eds.: Applications of Evolutionary Computing, EvoWorkshops 2005: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, EvoSTOC. Vol. 3449 of LNCS. Lausanne, Switzerland. Springer, 498–507

Sims, K. (1991). Artificial evolution for computer graphics. ACM Computer Graphics, 25: 319–328

Machado, P., Cardoso, A. (2002). All the truth about NEvAr. Applied Intelligence, Special Issue on Creative Systems, 16(2): 101–119

Greenfield, G. (2002). Color dependent computational aesthetics for evolving expressions. In Sarhangi, R., ed.: Bridges: Mathematical Connections in Art, Music, and Science; Conference Proceedings 2002. Winfield, Kansas. Central Plains Book Manufacturing, 9–16

Greenfield, G. (2003). Evolving aesthetic images using multiobjective optimization. In McKay, B., et al., eds.: Congress on Evolutionary Computation, CEC 2003. Vol. 3. Canberra, Australia. IEEE Press, 1903–1909

Svangård, N., Nordin, P. (2004). Automated aesthetic selection of evolutionary art by distance based classification of genomes and phenomes using the universal similarity metric. In Günther, R., et al., eds.: Applications of Evolutionary Computing, EvoWorkshops 2004: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, EvoSTOC. Vol. 3005 of LNCS. Coimbra, Portugal. Springer, 445–454

Baluja, S., Pomerlau, D., Todd, J. (1994). Towards automated artificial evolution for computer-generated images. Connection Science, 6(2): 325–354

Greenfield, G.R. (2002). On the co-evolution of evolving expressions. International Journal of Computational Intelligence and Applications, 2(1): 17–31

Todd, P.M., Werner, G.M. (1999). Frankensteinian approaches to evolutionary music composition. In Griffith, N., Todd, P.M., eds.: Musical Networks: Parallel Distributed Perception and Performance. MIT Press, 313–340

Birkhoff, G. (1933). Aesthetic Measure. Harvard University Press

Moles, A. (1958). Théorie de L’Information et Perception Esthétique. Denoel

Arnheim, R. (1956). Art and Visual Perception, a Psychology of the Creative Eye. Faber and Faber. London

Arnheim, R. (1966). Towards a Psychology of Art/Entropy and Art — An Essay on Disorder and Order. The Regents of the University of California

Arnheim, R. (1969). Visual Thinking. University of California Press. Berkeley, CA

Bense, M. (1965). Aesthetica. Einführung in die neue Aesthetik. Agis (reprinted by Baden-Baden, 1982)

Shannon, C.E. (1951). Prediction and entropy of printed english. Bell System Technical Journal, (30): 50–64

Staudek, T. (2002). Exact Aesthetics. Object and Scene to Message. PhD thesis. Faculty of Informatics, Masaryk University of Brno

Staudek, T., Linkov, V. (2004). Personality characteristics and aesthetic preference for chaotic curves. Journal of Mathematics and Design, 4(1): 297–304

Graves, M. (1948). Design Judgement Test. The Psychological Corporation. New York

Datta, R., Joshi, D., Li, J., Wang, J.Z. (2006). Studying aesthetics in photographic images using a computational approach. In: Computer Vision – ECCV 2006, 9th European Conference on Computer Vision, Part III. LNCS. Graz, Austria. Springer, 288–301

Aks, D., Sprott, J.C. (1996). Quantifying aesthetic preference for chaotic patterns. Empirical Studies of the Arts, 14: 1–16

Spehar, B., Clifford, C.W.G., Newell, N., Taylor, R.P. (2003). Universal aesthetic of fractals. Computers and Graphics, 27(5): 813–820

Wannarumon, S., Bohez, E.L.J. (2006). A new aesthetic evolutionary approach for jewelry design. Computer-Aided Design & Applications, 3(1-4): 385–394

Wertheimer, M. (1939). Laws of organization in perceptual forms. In Ellis, W.D., ed.: A Source Book of Gestalt Psychology. Harcourt Brace. New York, 71–88

Tyler, C.W., ed. (2002). Human Symmetry Perception and Its Computational Analysis. Lawrence Erlbaum Associates

Field, D.J., Hayes, A., Hess, R.F. (2000). The roles of polarity and symmetry in the perceptual grouping of contour fragments. Spatial Vision, 13(1): 51–66

Cope, D. (1992). On the algorithmic representation of musical style. In Laske, O., ed.: Understanding Music with AI: Perspectives on Music Cognition. MIT Press. Cambridge, Massachusetts, 354–363

Zipf, G.K. (1949). Human Behaviour and the Principle of Least Effort: An Introduction to Human Ecology. Addison-Wesley

Kittler, J. (1983). On the accuracy of the Sobel edge detector. Image Vision Computing, 1(1): 37–42

Zell, A., Mamier, G., Vogt, M., Mache, N., Hübner, R., Döring, S., Herrmann, K.U., Soyez, T., Schmalzl, M., Sommer, T., et al. (2003). SNNS: Stuttgart Neural Network Simulator User Manual, Version 4.2. Technical Report 3/92. University of Stuttgart. Stuttgart

Directmedia (2002). 10000 Meisterwerke der Malerei — von der Antike bis zum Beginn der Moderne. DVD-ROM


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