Automated Daily Production of Evolutionary Audio Visual Art — An Experimental Practice

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Tatsuo Unemi: Automated Daily Production of Evolutionary Audio Visual Art — An Experimental Practice. In: Computational Creativity 2014 ICCC 2014, 33-37.



Evolutionary computing based on computational aes- thetic measure as fitness criteria is one of the possi- ble methods to let the machine make art. The au- thor developed and set up a computer system that pro- duces ten short animations consisting sequences of ab- stract images and sound effects everyday. The produced pieces are published on the internet using three meth- ods, movie files, HTML5 + WebGL, and a special appli- cation software. The latter two methods provides view- ers experiences of a high resolution lossless animation. Their digest versions are also uploaded on a popular web service of movie sharing. It started October 2011. It is still in an experimental level that we need to brush up, but it has not always but often succeeded to engage the viewers.

Extended Abstract


author = {Tatsuo Unemi},
title = {Automated Daily Production of Evolutionary Audio Visual Art — An Experimental Practice},
booktitle = {Proceedings of the Fifth International Conference on Computational Creativity},
series = {ICCC2014},
year = {2014},
month = {Jun},
location = {Ljubljana, Slovenia},
pages = {33-37},
url = {,—_An_Experimental_Practice },
publisher = {International Association for Computational Creativity},
keywords = {computational, creativity},

Used References

Deb, K. 2001. Multi-Objective Optimization using Evolu- tionary Algorithms. John Wiley & Sons.

den Heijer, E., and Eiben, A. E. 2010. Using aesthetic mea- sures to evolve art. In WCCI 2010 IEEE World Congress on Computational Intelligence, 4533–4540.

den Heijer, E., and Eiben, A. E. 2014. Investigating aes- thetic measures for unsupervised evolutionary art. Swarm and Evolutionary Computation 16:52–68.

Galanter, P. 2003. What is generative art? complexity the- ory as a context for art theory. In Proceedings of the 6th Generative Art Conference, 76–99.

Galanter, P. 2012. Computational aesthetic evaluation: Past and future. In McCormack, J., and d’Inverno, M., eds., Com- puters and Creativity. London, UK: Springer-Verlag. chap- ter 10.

Koza, J. R. 1992. Genetic Programming: On The Program- ming of Computers by Means of Natural Selection. Cam- bridge, MA: MIT Press.

Machado, P., and Cardoso, A. 2002. All the truth about NEvAr. Applied Intelligence 16:101–118.

Machado, P.; Romero, J.; and Manaris, B. 2007. Exper- iments in computational aesthetics: An iterative approach to stylistic change in evolutionary art. In Romero, J., and Machado, P., eds., The Art of Artificial Evolution: A Hand- book on Evolutionary Art and Music. Berlin Heidelberg: Springer-Verlag. 381–415.

Matkovic, K.; Neumann, L.; Neumann, A.; Psik, T.; and Purgathofer, W. 2005. Global contrast factor – a new ap- proach to image contrast. In Computational Aesthetics 2005, 159–168.

Newman, M. E. J. 2006. Power laws, Pareto distributions and Zipf’s law. (cond-mat/0412004).

Pearson, M. 2011. Generative Art: A practical guide using Processing. Manning Publications.

Ross, B. J.; Ralph, W.; and Zong, H. 2006. Evolutionary image synthesis using a model of aesthetics. In WCCI 2006 IEEE World Congress on Computational Intelligence, 3832– 3839.

Satoh, H.; Ono, I.; and Kobayashi, S. 1997. A new gener- ation alternation model of genetic algorithms and its assess- ment. Journal of Japanese Society for Artificial Intelligence 12(5):734–744.

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

Takagi, H. 2001. Interactive evolutionary computation: Fu- sion of the capacities of EC optimization and human evalu- ation. Proceesings of the IEEE 89(9):1275–1296.

Unemi, T. 2009. Simulated breeding: A framework of breeding artifacts on the computer. In Komosinski, M., and Adamatzky, A. A., eds., Artificial Models in Software. Lon- don, UK: Springer-Verlag, 2 edition. chapter 12.

Unemi, T. 2010. SBArt4 – breeding abstract animations in realtime. In WCCI 2010 IEEE World Congress on Compu- tational Intelligence, 4004–4009.

Unemi, T. 2012a. SBArt4 for an automatic evolutionary art. In WCCI 2012 IEEE World Congress on Computational Intelligence, 2014–2021.

Unemi, T. 2012b. Synthesis of sound effects for genera- tive animation. In Proceedings of the 15th Generative Art Conference, 364–376.

Unemi, T. 2013. Non-stop evolutionary art you are embed- ded in. In Proceedings of the 16th Generative Art Confer- ence, 247–253.


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