Interactive Evolution of Camouflage
Inhaltsverzeichnis
Reference
Reynolds, C.: Interactive Evolution of Camouflage. In: Proceedings of the 12th International Conference on the Synthesis and Simulation of Living Systems (ALife XII). MIT Press, Cambridge (2010).
DOI
http://dx.doi.org/10.1162/artl_a_00023
Abstract
This article presents an abstract computation model of the evolution of camouflage in nature. The 2D model uses evolved textures for prey, a background texture representing the environment, and a visual predator. A human observer, acting as the predator, is shown a cohort of 10 evolved textures overlaid on the background texture. The observer clicks on the five most conspicuous prey to remove (“eat”) them. These lower-fitness textures are removed from the population and replaced with newly bred textures. Biological morphogenesis is represented in this model by procedural texture synthesis. Nested expressions of generators and operators form a texture description language. Natural evolution is represented by genetic programming (GP), a variant of the genetic algorithm. GP searches the space of texture description programs for those that appear least conspicuous to the predator.
Extended Abstract
Bibtex
Used References
1. Abbott, K. (2010). Background evolution in camouflage systems: A predator-prey/pollinator-flower game. Journal of Theoretical Biology, 262(4), 662–678.
2. Amazon Mechanical Turk. (2005). http://www.mturk.com/ (accessed 2010).
3. Beddard, F. E. (1895). Animal coloration. An account of the principal facts and theories relating to the colours and marking of animals. London: Swan Sonnenschein & Co.
4. Bond, A. B., & Kamil, A. C. (2002). Visual predators select for crypticity and polymorphism in virtual prey. Nature, 415(6872), 609–613.
5. Chu, H.-K., Hsu, W.-H., Mitra, N. J., Cohen-Or, D., Wong, T.-T., & Lee, T.-Y. (2010). Camouflage images. ACM Transactions on Graphics, 29(4), article 51.
6. Cott, H. B. (1940). Adaptive coloration in animals. London: Methuen & Co.
7. Cuthill, I. C., Stevens, M., Sheppard, J., Maddocks, T., Parraga, C. A., & Troscianko, T. S. (2005). Disruptive coloration and background pattern matching. Nature, 434(7029), 72–74.
8. Cuthill, I. C., Hiby, E., & Lloyd, E. (2006). The predation costs of symmetrical cryptic coloration. Proceedings of the Royal Society B, 273(1591), 1267–1271.
9. Darwin, C. (1859). On the origin of species by means of natural selection. London: John Murray.
10. Dawkins, R. (1986). The blind watchmaker. New York: W. W. Norton & Co.
11. Ebert, D. S., Musgrave, F. K., Peachey, D., Perlin, K., & Worley, S. (1994). Texturing and modeling: A procedural approach. Boston: AP Professional.
12. Eizirik, E., David, V., Buckley-Beason, V., Roelke, M., Schaffer, A., Hannah, S., Narfstrom, K., OʼBrien, S., & Menotti-Raymond, M. (2010). Defining and mapping mammalian coat pattern genes: Multiple genomic regions implicated in domestic cat stripes and spots. Genetics, 184(1), 267–275.
13. Endler, J. A. (1980). Natural selection on color patterns in Poecilia reticulata. Evolution, 34(1), 76–91.
14. Funes, P., Sklar, E., Juillé, H., & Pollack, J. (1998). Animal-animat coevolution: Using the animal population as fitness function. In R. Pfeifer, B. Blumberg, J.-A. Meyer, & S. W. Wilson (Eds.), From Animals to Animats 5: Proceedings of the Fifth International Conference on Simulation of Adaptive Behavior (pp. 525–533). Cambridge, MA: MIT Press.
15. Gagné, C., & Parizeau, M. (2006). Genericity in evolutionary computation software tools: Principles and case-study. International Journal on Artificial Intelligence Tools, 15(2), 173–194.
16. Google Image Labeler. (2006). http://images.google.com/imagelabeler/ (accessed 2010).
17. Holland, J. H. (1975). Adaptation in natural and artificial systems. Ann Arbor, MI: University of Michigan Press.
18. Itti, L., Koch, C., & Niebur, E. (1998). A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11), 1254–1259.
19. Kashtan, N., Noor, E., & Alon, U. (2007). Varying environments can speed up evolution. Proceedings of the National Academy of Sciences of the U.S.A., 104(34), 13711–13716.
20. Koza, J. R. (1992). Genetic programming: On the programming of computers by means of natural selection. Cambridge, MA: MIT Press.
21. Merilaita, S. (2003). Visual background complexity facilitates the evolution of camouflage. Evolution, 57(6), 1248–1254.
22. Montana, D. J. (1995). Strongly typed genetic programming. Evolutionary Computation, 3(2), 199–230.
23. Open BEAGLE. (2002) Open BEAGLE evolutionary computation library, version 3.0.3. Web site for code and documentation, http://beagle.gel.ulaval.ca/ (accessed 2010).
24. Perlin, K. (1985). An image synthesizer. In B. A. Barsky (Ed.), Proceedings of the 12th Annual Conference on Computer Graphics and Interactive Techniques SIGGRAPH ʼ85 (pp. 287–296). New York: ACM.
25. Reynolds, C. (2009) Texture synthesis diary. Blog/lab notebook: http://www.red3d.com/cwr/texsyn/diary.html (accessed 2014-11-02).
26. Schaefer, H. M., & Stobbe, N. (2006). Disruptive coloration provides camouflage independent of background matching. Proceedings of the Royal Society B, 273(1600), 2427–2432.
27. Sims, K. (1991). Artificial evolution for computer graphics. In T. W. Sederberg (Ed.), Proceedings of the 18th Annual Conference on Computer Graphics and Interactive Techniques SIGGRAPH ʼ91 (pp. 319–328). New York: ACM.
28. Sherratt, T. N., Pollitt, D., & Wilkinson, D. M. (2007). The evolution of crypsis in replicating populations of web-based prey. Oikos, 116(3), 449–460.
29. Stevens, M., & Cuthill, I. C. (2006). Disruptive coloration, crypsis and edge detection in early visual processing. Proceedings of the Royal Society B, 273(1598), 2141–2147.
30. Stevens, M., & Merilaita, S. (2009). Animal camouflage: Current issues and new perspectives. Proceedings of the Royal Society B, 364(1516), 423–427.
31. Takagi, H. (2001). Interactive evolutionary computation: Fusion of the capabilities of EC optimization and human evaluation. Proceedings of the IEEE, 89(9), 1275–1296.
32. Togelius, J., Yannakakis, G., Stanley, K., & Browne, C. (2010). Search-based procedural content generation. In C. Di Chio et al. (Eds.), Proceedings of 2nd European Event on Bio-inspired Algorithms in Games, EvoGAMES 2010 (pp. 141–150). Berlin: Springer LNCS.
33. Thayer, G. H. (1909). Concealing-coloration in the animal kingdom: An exposition of the laws of disguise through color and pattern: Being a summary of Abbott H. Thayerʼs discoveries. New York: Macmillan.
34. Unemi, T. (2003). Simulated breeding—A framework of breeding artifacts on the computer. Kybernetes, 32(1/2), 203–220.
35. von Ahn, L., & Dabbish, L. (2008). Designing games with a purpose. Communications of the ACM, 51(8), 58–67.
36. Wei, L.-Y., Lefebvre, S., Kwatra, V., & Turk, G. (2009). State of the art in example-based texture synthesis. In Eurographics ʼ09 State of the Art Reports (STARs). Goslar: Eurographics Association.
37. Wilson, S. W. (2009). Coevolution of pattern generators and recognizers. TR 2009006. Illinois Genetic Algorithms Laboratory.
Links
Full Text
http://www.red3d.com/cwr/iec/2011ReynoldsALifeJournal.pdf
