Evolution and Collective Intelligence of the Electric Sheep

Aus de_evolutionary_art_org
Version vom 2. November 2015, 22:16 Uhr von Gubachelier (Diskussion | Beiträge)

(Unterschied) ← Nächstältere Version | Aktuelle Version (Unterschied) | Nächstjüngere Version → (Unterschied)
Wechseln zu: Navigation, Suche


Draves, Scott: Evolution and Collective Intelligence of the Electric Sheep. In: Romero, Juan; Machado, Penousal: The Art of Artificial Evolution. Springer, Berlin, 2007, S. 63-78.




Electric Sheep is a collective intelligence composed of 40,000 computers and people mediated by a genetic algorithm. It is made with an open source screen-saver that harnesses idle computers into a render farm with the purpose of animating and evolving artificial life-forms known as sheep. The votes of the users form the basis for a fitness function for exploring a space of abstract animations. Users also may design sheep by hand for inclusion in the gene pool.

The name Electric Sheep is an homage to Philip K. Dick’s novel Do Androids Dream of Electric Sheep; the basis for the film Blade Runner. The metaphor compares the screen-saver to the computer’s dream.

After the introduction, we dig into the system starting with Sect. 3.2 on its architecture and implementation. Sect. 3.3 covers the genetic code, including its basis in the equations of classic Iterated Function Systems. The equation is then generalized into the Fractal Flame algorithm, which translates the genetic code into an image. The next two sections treat color and motion.

Section 3.4 shows how the genetic algorithm decides which sheep die, which ones reproduce, and how. Section 3.5. defines the primary dataset and its limitations, and reports some of its statistics. Section 3.5.1 uses the dataset to determine that the genetic algorithm functions more as an amplifier of its human collaborators’ creativity rather than as a traditional genetic algorithm that optimizes a fitness function.

The goal of Electric Sheep is to create a self-supporting, network-resident life-form. Section 3.6. speculates on how to make the flock support more of a self-sustaining reaction rather than functioning as an amplifier. Finally, Sect. 3.6.1, explains how Dreams in High Fidelity addresses the support issue.

Extended Abstract


booktitle={The Art of Artificial Evolution},
series={Natural Computing Series},
editor={Romero, Juan and Machado, Penousal},
title={Evolution and Collective Intelligence of the Electric Sheep},
url={http://dx.doi.org/10.1007/978-3-540-72877-1_3 http://de.evo-art.org/index.php?title=Evolution_and_Collective_Intelligence_of_the_Electric_Sheep },
publisher={Springer Berlin Heidelberg},
author={Draves, Scott},

Used References

Draves, S. (2005). The electric sheep screen-saver: A case study in aesthetic evolution. In: Applications of Evolutionary Computing, LNCS 3449. Springer

Anderson, D., et al. (2002). SETI@home: An experiment in public-resource computing. Communications of the ACM, 45: 56–61

Sims, K. (1991). Artificial evolution for computer graphics. In: Proceedings of SIGGRAPH. ACM

Townsend, M. (2004). Apophysis. http://apophysis.org

Cohen, B. (2003). Incentives build robustness in bittorrent. In: Workshop on Economics of Peer-to-Peer Systems

Parens, B. (1999). The open source definition. In: Open Sources: Voices from the Open Source Revolution. O’Reilly

Draves, S. (2004). The fractal flame algorithm. http://flam3.com/flame.pdf

Barnsley, M. (1988). Fractals Everywhere. Academic Press

Lessig, L. (2002). The Future of Ideas. Vintage

Balakrishnan, H., Kaashoek, M.F., Karger, D., Morris, R., Stoica, I. (2003). Looking up data in p2p systems. Communications of the ACM, 46(2): 43–48

Turing, A. (1950). Can a machine do anything new? Computing Machinery and Intelligence, 59: 433–460

Sprott, J.C. (1994). Automatic generation of iterated function systems. Computers and Graphics, 18: 417–425

Draves, S., Abraham, F., Abraham, R., Sprott, J.C., Viotti, P. (2005). Aesthetics and the fractal dimension of electric sheep. Presented at the annual meeting of the Society for Chaos Theory in Psychology and the Life Sciences

Koza, J. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. Complex Adaptive Systems. The MIT Press


Full Text


intern file

Sonstige Links