Toward genetic aesthetics: mutation of bio information and generative art system

Aus de_evolutionary_art_org
Wechseln zu: Navigation, Suche


Jongcheon Shin, Joonsung Yoon: Toward genetic aesthetics: mutation of bio information and generative art system. In: Generative Art 2014.



We propose the meaning and potential of “genetic aesthetics,” because bio information can inspire the aesthetic purpose of generative art. By examining the definition of generative art and the term generative, the conditions of generative art can be compressed as rule, autonomy, and system. Among them, a system is considered as a key element in generative art, because an artist transfers subsequent control to system. In particular, a genetic system is regarded as the highest position on the Gary Flake’s graph of complexity. The graph shows that truly complex things occur at a transition point between orderly things and random things. It is a nexus of bio information and generative aesthetics, because it confirms that unity and diversity are not mutually exclusive concepts. Here, noise of information theory and a mutation of biology have an important role to explain the aesthetic value within generative art. Thus, we analyze noise by using the Shannon’s binary entropy function, and then apply a mutation to that function. The analysis shows that the uncertainty due to mutations can create the biological complexity in keeping with the certainty due to redundancy. A mutation might be a factor to produce probabilities of innovation or deviation under the well-knit database of bio information. Bio information in terms of a mutation eventually can be more persuasive to explain the aesthetic value of generative art in that the aim of generative aesthetics is the artificial production of probabilities of innovation or deviation from the norm. A specific process that can lie beyond the artist's intuition can be derived from a specific factor such as a mutation. It can inspire computer-based generative art in the relative discussions on the noise of complex system. Accordingly, genetic aesthetics can present the ultimate aesthetic direction at which generative art aims.

Extended Abstract


Used References

1. Soddu, Celestino. “Argenia, a Natural Generative Design.” GA1998-1st Generative Art Conference, 1998.

2. Maturana, Humberto R. Autopoiesis and cognition: The realization of the living. Vol. 42. Springer, 1980.

3. Galanter, Philip. “What is Generative Art? : Complexity Theory as a Context for Art theory.” GA2003–6th Generative Art Conference, 2003.

4. Mitchell, WJ Thomas., and Hansen, Mark BN., eds. Critical terms for media studies. University of Chicago Press, 2010.

5. Flake, Gary William. The computational beauty of nature: Computer explorations of fractals, chaos, complex systems, and adaptation. MIT press, 1998.

6. Krane, Dan E. and Raymer, Michael L. Fundamental concepts of bioinformatics. Pearson Education India, 2003.

7. Campbell, Neil A. and Reece, Jane B. Biology, 8th Ed., Benjamin Cumming's Publishing Company, 2008.

8. Seife, Charles. Decoding the Universe: How the New Science of Information Is Explaining Everything in the Cosmos, Penguin, 2007.

9. Shannon, Claude E. “A Mathematical Theory of Communication.” The Bell System Technical Journal, Vol. 27, No. 3, 1948, pp.379-423.

10. Ma, GuoJi., Liang, L., Fan, Y., Wang, W., Dai, J., and Yuan, Z. “The entropy characters of point mutation.” Chinese Science Bulletin, Vol. 53, 2008, pp.3008-3015.

11. Bense, Max. and Reichardt, Jaisa. “The projects of generative aesthetics.” Cybernetics, Art, and Ideas, New York Graphic Society, 1971, pp.57-60.

12. Grau, Oliver, and Veigl Thomas, eds. Imagery in the 21st Century. Mit Press, 2011.

13. Shusterman, Richard. Performing live: Aesthetic alternatives for the ends of art. Cornell University Press. 2000.


15. Soddu, Celestino. and Colabela, Enrica. “Why Generative Art?” GA2013-16th Generative Art Conference, 2013.


Full Text

intern file

Sonstige Links