Truth to Process – Evolutionary Art and the Aesthetics of Dynamism
Inhaltsverzeichnis
Reference
Philip Galanter: Truth to Process – Evolutionary Art and the Aesthetics of Dynamism. In: Generative Art 2009.
DOI
Abstract
After a great deal of initial promise and enthusiasm, evolutionary art seems to have hit a premature and disappointing plateau. This paper proposes a difficult but necessary program for the advancement of evolutionary art.
First two significant problems are discussed. The first is the problem of creating aesthetic fitness functions that would allow evolutionary art systems to execute unattended with industrial sized populations and generations. In this context a quick survey of computational aesthetic evaluation is offered. Then it is suggested that progress in perceptual psychology and neuroaesthetics, coupled with advancements in connectionist computing, may provides new techniques for scoring aesthetic fitness.
The second problem is the sense of “sameness” and lack of innovation exhibited by typical evolutionary art systems. In this regard the related technical problem of genetic representation is noted, various types of genetic representation are reviewed, and a critique is offered relative to evolution in nature. It is noted that evolution in exercises multiple levels of complexification and emergence. Finally an implication for generative art aesthetics and theory is discussed. It is suggested that current evolutionary art systems and projects are incoherent in so far as they don’t focus on the essential virtue of generative art, i.e. a focus on process rather than final form. It is suggested that the general shift from nouns to verbs that is essential to generative art should be pushed to the fore. An aesthetic of truth to process and dynamism is proposed as foundational for evolutionary art.
Extended Abstract
Bibtex
Used References
[1] Todd, S. and W. Latham, Evolutionary art and computers. 1992, London ; San Diego: Academic Press. 224 p., 32 p. of plates.
[2] Fogel, L.J., Intelligence through simulated evolution : forty years of evolutionary programming. Wiley series on intelligent systems. 1999, New York: Wiley. xiii, 162.
[3] Lewis, M., Evolutionary Visual Art and Design, in The art of artificial evolution : a handbook on evolutionary art and music, J. Romero and P. Machado, Editors. 2008, Springer: Berlin. p. 3-37.
[4] Draves, S., The electric sheep screen-saver: A case study in aesthetic evolution. Applications of Evolutionary Computing, Proceedings, 2005. 3449: p. 458- 467.
[5] Ross, A., Poll stars. Artforum International, 1995. 33(5).
[6] Aldiss, B., The mechanical Turk - The true story of the chess-playing machine that changed the world. Tls-the Times Literary Supplement, 2002(5170): p. 33-33.
[7] Saunders, R. and J.S. Gero, Curious agents and situated design evaluations. Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing, 2004. 18(2): p. 153-161.
[8] Greenfield, G., Robot paintings evolved using simulated robots. Applications of Evolutionary Computing, Proceedings, 2006. 3907: p. 611-621.
[9] Jaskowski, W., Learning and recognition of hand-drawn shapes using generative genetic programming. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2007. 4448 LNCS: p. 281-290.
[10] Fornari, J., Creating soundscapes using evolutionary spatial control. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2007. 4448 LNCS: p. 517-526.
[11] Ciesielski, V., Evolution of animated photomosaics. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2007. 4448 LNCS: p. 498-507.
[12] McDermott, J., N.J.L. Griffith, and M. O'Neill, Toward user-directed evolution of sound synthesis parameters. Applications of Evolutionary Computing, Proceedings, 2005. 3449: p. 517-526.
[13] Khalifa, Y. and R. Foster, A two-stage autonomous evolutionary music composer. Applications of Evolutionary Computing, Proceedings, 2006. 3907: p. 717-721.
[14] Manaris, B., et al., Developing fitness functions for pleasant music: Zipf's law and interactive evolution systems. Applications of Evolutionary Computing, Proceedings, 2005. 3449: p. 498-507.
[15] Mori, T., Y. Endou, and A. Nakayama, Fractal analysis and aesthetic evaluation of geometrically overlapping patterns. Textile Research Journal, 1996. 66(9): p. 581-586.
[16] Taylor, R.P., Chaos, Fractals, Nature: a new look at Jackson Pollock. 2006, Eugene, USA: Fractals Research.
[17] Birkhoff, G.D., Aesthetic measure. 1933, Cambridge, Mass.,: Harvard University Press. xii, 1 p., 2 l., 3-225, 1 p.
[18] Machado, P., Computing aesthetics. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 1998. 1515.
[19] Machado, P., J. Romero, and B. Manaris, Experiments in Computational Aesthetics, in The art of artificial evolution : a handbook on evolutionary art and music, J. Romero and P. Machado, Editors. 2008, Springer: Berlin. p. 417-451.
[20] Phon-Amnuaisuk, S., Evolving music generation with SOM-fitness genetic programming. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2007. 4448 LNCS: p. 557-566.
[21] Gedeon, T. and D. s, Neural network for modeling esthetic selection. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008. 4985 LNCS(PART 2): p. 666-674.
[22] Takagi, H., Interactive evolutionary computation: Fusion of the capabilities of EC optimization and human evaluation. Proceedings of the Ieee, 2001. 89(9): p. 1275-1296.
[23] Yuan, J., Large population size IGAs with individuals' fitness not assigned by user. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008. 5227 LNAI: p. 267- 274.
[24] Machado, P., et al., Partially Interactive Evolutionary Artists. New Generation Computing, 2005. 23(2): p. 143-155.
[25] Machwe, A.T., Towards an interactive, generative design system: Integrating a 'build and evolve' approach with machine learning for complex freeform design. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2007. 4448 LNCS: p. 449-458.
[26] Martindale, C., A Neural-Network Theory of Beauty, in Evolutionary and neurocognitive approaches to aesthetics, creativity, and the arts, C. Martindale, P. Locher, and V.M. Petrov, Editors. 2007, Baywood Pub.: Amityville, N.Y. p. 181-194.
[27] Hawkins, J. and S. Blakeslee, On intelligence. 1st ed. 2004, New York: Times Books. 261 p.
[28] Casti, J.L., Complexification : explaining a paradoxical world through the science of surprise. 1st ed. 1994, New York, NY: HarperCollins. xiv, 320 p.
[29] Kauffman, S.A., At home in the universe : the search for laws of self- organization and complexity. 1995, New York: Oxford University Press. viii, 321.
[30] Galanter, P., What is Generative Art? Complexity theory as a context for art theory, in International Conference on Generative Art. 2003, Generative Design Lab, Milan Polytechnic: Milan, Italy.
[31] Galanter, P., Complexism and the role of evolutionary art, in The art of artificial evolution : a handbook on evolutionary art and music, J. Romero and P. Machado, Editors. 2008, Springer: Berlin. p. 311-332.
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Full Text
http://www.generativeart.com/on/cic/GA2009Papers/p20.pdf
http://philipgalanter.com/downloads/ga2009_truth_to_process.pdf
