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Evolution and Growth of Virtual Plants

  • Marc Ebner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2801)

Abstract

According to the Red Queen hypothesis, an evolving population may be improving some trait, even though its fitness remains constant. We have created such a scenario with a population of coevolving plants. Plants are modeled using Lindenmayer systems and rendered with OpenGL. The plants consist of branches and leaves. Their reproductive success depends on their ability to catch sunlight as well as their structural complexity. All plants are evaluated inside the same environment, which means that one plant is able to cover other plants leaves. Leaves which are placed in the shadow of other plants do not catch any sunlight. The shape of the plant also determines the area where offspring can be placed. Offspring can only be placed in the vicinity of a plant. A number of experiments were performed in different environments. The Red Queen effect was seen in all cases.

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References

  1. 1.
    Beneš, B.: An efficient estimation of light in simulation of plant development. In: Boulic, R., Hegron, G. (eds.) Computer Animation and Simulation 1996, pp. 153–165. Springer, Berlin (1996)Google Scholar
  2. 2.
    Dawkins, R., Krebs, J.R.: Arms races between and within species. Proc. R. Soc. Lond. B 205, 489–511 (1979)CrossRefGoogle Scholar
  3. 3.
    Deussen, O., Hanrahan, P., Lintermann, B., Měch, R., Pharr, M., Prusinkiewicz, P.: Realistic modeling and rendering of plant ecosystems. In: SIGGRAPH 1998 Conf. Proceedings, Comp. Graphics, Orlando, FL, pp. 275–286. ACM Press, New York (1998)Google Scholar
  4. 4.
    Ebner, M., Grigore, A., Heffner, A., Albert, J.: Coevolution produces an arms race among virtual plants. In: Foster, J.A., Lutton, E., Miller, J., Ryan, C., Tettamanzi, A.G.B. (eds.) EuroGP 2002. LNCS, vol. 2278, p. 316. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  5. 5.
    Jacob, C.: Genetic L-system programming. In: Davudor, Y., Schwefel, H.-P., Männer, R. (eds.) Parallel Problem Solving from Nature – PPSN III. The Third Int. Conf. on Evolutionary Computation, Jerusalem, Israel, pp. 334–343. Springer, Berlin (1994)Google Scholar
  6. 6.
    Jacob, C.: Evolution programs evolved. In: Voigt, H.-M., Ebeling, W., Rechenberg, I., Schwefel, H.-P. (eds.) Parallel Problem Solving from Nature – PPSN IV. The Fourth Int. Conf. on Evolutionary Computation, Berlin, Germany, pp. 42–51. Springer, Berlin (1996)CrossRefGoogle Scholar
  7. 7.
    Jacob, C.: Evolving evolution programs: Genetic programming and L-systems. In: Koza, J.R., Goldberg, D.E., Fogel, D.B., Riolo, R.L. (eds.) Proc. of the First Annual Conf. on Genetic Programming, pp. 107–115. The MIT Press, Cambridge (1996)Google Scholar
  8. 8.
    Jacob, C.: Evolution and coevolution of developmental programs. Computer Physics Communications, 46–50 (1999)Google Scholar
  9. 9.
    Jacob, C.: Illustrating Evolutionary Computation with Mathematica. Morgan Kaufmann Publishers, San Francisco (2001)Google Scholar
  10. 10.
    Kim, J.T.: Lindevol: Artificial models for natural plant evolution. Künstliche Intelligenz 1, 26–32 (2000)Google Scholar
  11. 11.
    Kókai, G., Tóth, Z., Vänyi, R.: Application of genetic algorithms with more populations for Lindenmayer systems. In: Alpaydin, E., Fyfe, C. (eds.) Int. ICSC Symposium on Engineering of Int. Systems EIS 1998, Tenerife, Spain. University of La Laguna, pp. 324–331. ICSC Academic Press, Canada (1998)Google Scholar
  12. 12.
    Kókai, G., Tóth, Z., Ványi, R.: Evolving artificial trees described by parametric L-systems. In: Proc. of the 1999 IEEE Canadian Conf. on Electrical and Computer Engineering. Shaw Conference Center, Edmonton, Alberta, Canada, pp. 1722–1727. IEEE Press, Los Alamitos (1999)Google Scholar
  13. 13.
    Koza, J.R.: Genetic Programming. In: On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge (1992)Google Scholar
  14. 14.
    Koza, J.R.: Genetic Programming II. In: Automatic Discovery of Reusable Programs. The MIT Press, Cambridge (1994)Google Scholar
  15. 15.
    Mock, K.J.: Wildwood: The evolution of L-system plants for virtual environments. In: Int. Conf. on Evolutionary Computation, Anchorage, Alaska, pp. 476–480 (1998)Google Scholar
  16. 16.
    Niklas, K.J.: Computer-simulated plant evolution. Scientific American 254(3), 68–75 (1986)CrossRefGoogle Scholar
  17. 17.
    Ochoa, G.: On genetic algorithms and Lindenmayer systems. In: Parallel Problem Solving from Nature – PPSN V, pp. 335–344. Springer, Berlin (1998)CrossRefGoogle Scholar
  18. 18.
    Perlin, K.: Noise, hypertexture, antialiasing and gesture. In: Ebert, D.S., Musgrave, F.K., Peachey, D., Perlin, K., Worley, S. (eds.) Texturing and Modeling: A Procedural Approach, 2nd edn., pp. 209–274. AP Professional, Cambridge (1998)Google Scholar
  19. 19.
    Prusinkiewicz, P., Lindenmayer, A.: The Algorithmic Beauty of Plants. Springer, New York (1990)zbMATHGoogle Scholar
  20. 20.
    Van Valen, L.: A new evolutionary law. Evolutionary Theory 1, 1–30 (1973)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Marc Ebner
    • 1
  1. 1.Universität WürzburgLehrstuhl für Informatik IIWürzburgGermany

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