Mechanisms for Complex Systems Engineering Through Artificial Development

Chapter
Part of the Understanding Complex Systems book series (UCS)

Abstract

We argue that artificial development is an appropriate means of approaching complex systems engineering. Artificial development works via the inclusion of mechanisms that enhance the evolvability of a design space. Two of these mechanisms, regularities and adaptive feedback with the environment, are discussed. We concentrate on the less explored of the two: adaptive feedback. A concrete example is presented and applied to a simple artificial problem resembling vasculogenesis. It is shown that the use of a local feedback function substantively improves the efficacy of a machine learner on the problem. Further, inclusion of this adaptive feedback eliminates the sensitivity of the machine learner to a system parameter previously shown to correspond to problem hardness.

Notes

Acknowledgments

TK and WB would like to thank Nature for billions of years of tireless effort. Smashing stuff! In addition, NSERC of Canada supported WB by Discovery Grant RGPIN 283304-07.

References

  1. 1.
    Arthur, W.: The effect of development on the direction of evolution: toward a twenty-first century consensus. Evol. Dev. 6(4), 282–288 (2004)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Banzhaf, W., Pillay, N.: Why complex systems engineering needs biological development. Complexity 13(2), 12–21 (2007)CrossRefGoogle Scholar
  3. 3.
    Britton, N., Brown, A.: An Illustrated Ora of the Northern United States, Canada and the British Possessions, vol. 2. C. Scribner’s Sons, New York (1913)Google Scholar
  4. 4.
    Coen, E.: The Art of Genes: How Organisms Make Themselves. Oxford University Press, Oxford (1999)Google Scholar
  5. 5.
    Crews, D., Bull, J.: Sex determination: some like it hot (and some don’t). Nature 451, 527–528 (2008). doi: 10.1038/451527a Google Scholar
  6. 6.
    Dawkins, R.: Climbing Mount Improbable. W.W. Norton & Company, New York (1996)Google Scholar
  7. 7.
    Devert, A., Bredeche, N., Schoenauer, M.: Artificial ontogeny for truss structure design. In: IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops, vol. 0, pp. 298–305 (2008). doi: 10.1109/SASOW.2008.53
  8. 8.
    Federici, D., Downing, K.: Evolution and development of a multicellular organism: scalability, resilience, and neutral complexification. Artif. Life 12(3), 381–409 (2006)CrossRefGoogle Scholar
  9. 9.
    Gilbert, S.F., Epel, D.: Ecological Developmental Biology: Integrating Epigenetics, Medicine, and Evolution. Sinauer Associates Inc., Sunderland (2009)Google Scholar
  10. 10.
    Gould, S.J.: The Structure of Evolutionary Theory. The Belknap Press of Harvard University Press, Cambridge (2002)Google Scholar
  11. 11.
    Gruau, F., Whitley, D., Pyeatt, L.: A comparison between cellular encoding and direct encoding for genetic neural networks. In: GECCO ’96: Proceedings of the First Annual Conference on Genetic Programming, pp. 81–89. MIT Press, Cambridge, MA, USA (1996)Google Scholar
  12. 12.
    Harding, S., Miller, J.: The dead state: a comparison between direct and developmental encodings. In: GECCO ’06: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation (2006)Google Scholar
  13. 13.
    Hendrikse, J.L., Parsons, T., Hallgrmsson, B.: Evolvability as the proper focus of evolutionary developmental biology. Evol. Dev. 9(4), 393–401 (2007). doi: 10.1111/j.1525-142X.2007.00176.x Google Scholar
  14. 14.
    Hornby, G.: Measuring, enabling and comparing modularity, regularity and hierarchy in evolutionary design. In: GECCO ’05: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, pp. 1729–1736. ACM, New York, NY, USA (2005). doi: 10.1145/1068009.1068297
  15. 15.
    Huttenlocher, P.R.: Neural Plasticity: The Effects of Environment on the Development of the Cerebral Cortex. Harvard University Press, Cambridge (2002)Google Scholar
  16. 16.
    Ilachinski, A.: Cellular Automata: A Discrete Universe. World Scientific, Singapore (2001)Google Scholar
  17. 17.
    Kicinger, R., Arciszewski, T., Jong, K.A.D.: Morphogenic evolutionary design: cellular automata representations in topological structural design. In: Parmee I.C. (ed.) Adaptive Computing in Design and Manufacture VI. Springer-Verlag (2004)Google Scholar
  18. 18.
    Kicinger, R., Arciszewski, T., Jong, K.A.D.: Evolutionary computation and structural design: a survey of the state of the art. Comput. Struct. 83(23–24), 1943–1978 (2005)CrossRefGoogle Scholar
  19. 19.
    Kowaliw, T.: A good number of forms fairly beautiful. Ph.D. thesis, Concordia University, Montréal, QC, Canada (2007)Google Scholar
  20. 20.
    Kowaliw, T.: Measures of complexity for artificial embryogeny. In: GECCO ’08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp. 843–850. ACM, New York, NY, USA (2008). doi: 10.1145/1389095.1389259
  21. 21.
    Kowaliw, T., Banzhaf, W.: Augmenting artificial development with local fitness. In: Tyrrell A. (ed.) IEEE Congress on Evolutionary Computation (CEC), 2009, pp. 316–323. doi: 10.1109/CEC.2009.4982964
  22. 22.
    Kowaliw, T., Grogono, P., Kharma, N.: Bluenome: a novel developmental model of artificial morphogenesis. In: Deb, K. et al. (eds.) GECCO ’04: Proceedings of the 6th Annual Conference on Genetic and Evolutionary Computation. Springer-Verlag (2004)Google Scholar
  23. 23.
    Kowaliw, T., Grogono, P., Kharma, N.: Environment as a spatial constraint on the growth of structural form. In: GECCO ’07: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, pp. 1037–1044. ACM, New York, NY, USA (2007). doi: 10.1145/1276958.1277163
  24. 24.
    Kowaliw, T., Grogono, P., Kharma, N.: The evolution of structural form through artificial embryogeny. In: IEEE Symposium on Artificial Life, ALIFE ’07, pp. 425–432. IEEE (2007). doi: 10.1109/ALIFE.2007.367826
  25. 25.
    Lindenmayer, A.: Mathematical models for cellular interaction in development. J. Theor. Biol. 18, 280–315 (1968)CrossRefGoogle Scholar
  26. 26.
    Lipson, H.: Principles of modularity, regularity, and hierarchy for scalable systems. J. Biol. Phys. Chem. 7, 125–128 (2007)CrossRefGoogle Scholar
  27. 27.
    Mech, R., Prusinkiewicz, P.: Visual models of plants interacting with their environment. In: SIGGRAPH ’96 Proceedings, vol. 30, pp. 397–410 (1996)Google Scholar
  28. 28.
    Merks, R., Roeland, M., Hoekstra, A., Kaandorp, J., Sloot, P., Hogeweg, P.: Problem-solving environments for biological morphogenesis. Comput. Sci. Eng. 8(1), 61–72 (2006). doi: 10.1109/MCSE.2006.11 Google Scholar
  29. 29.
    Miller, J.: Evolving a self-repairing, self-regulating, french flag organism. In: Deb, K. et al. (eds.) GECCO ’04: Proceedings of the 6th Annual Conference on Genetic and Evolutionary Computation, pp. 129–139. Springer-Verlag (2004)Google Scholar
  30. 30.
    Mulder, B.: On growth and force. Science 322, 1643–1644 (2008)CrossRefGoogle Scholar
  31. 31.
    Newman, S.A., Forgacs, G., Müller, G.B.: Before programs: the physical origination of multicellular forms. Int. J. Dev. Biol. 50, 289–299 (2006). doi: 10.1387/ijdb.052049sn Google Scholar
  32. 32.
    Rieffel, J., Pollack, J.: The emergence of ontogenic scaffolding in a stochastic development environment. In: Deb, K. et al. (eds.) GECCO ’04: Proceedings of the 6th Annual Conference on Genetic and Evolutionary Computation. Springer-Verlag (2004)Google Scholar
  33. 33.
    Sen, S., Day, A.: Modelling trees and their interaction with the environment: a survey. Comput. Graph. 29(5), 805–817 (2005). doi: 10.1016/j.cag.2005.08.025 Google Scholar
  34. 34.
    Seys, C., Beer, R.: Effect of encoding on the evolvability of an embodied neural network. In: GECCO ’06: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation (2006)Google Scholar
  35. 35.
    Stanley, K., D’Ambrosio, D., Gauci, J.: A hypercube-based encoding for evolving large-scale neural networks. Artif. Life 15(2), 185–212 (2009). doi: 10.1162/artl.2009.15.2.15202 Google Scholar
  36. 36.
    Steiner, T., Jin, Y., Sendhoff, B.: A cellular model for the evolutionary development of lightweight material with an inner structure. In: GECCO ’08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp. 851–858. ACM (2008). doi: 10.1145/1389095.1389260
  37. 37.
    Tufte, G., Haddow, P.C.: Extending artificial development: exploiting environmental information for the achievement of phenotypic plasticity. In: Evolvable Systems: From Biology to Hardware (2007)Google Scholar
  38. 38.
    Turing, A.: The chemical basis of morphogenesis. Philos. Trans. Royal Soc. B 237, 37–72 (1952)CrossRefGoogle Scholar
  39. 39.
    Wolpert, L., Dover, G.: Positional information and pattern formation. Philos. Trans. Royal Soc. Lond. Ser. B Biol. Sci. 295(1078), 441–450 (1981)Google Scholar
  40. 40.
    Yogev, O., Shapiro, A., Antonsson, E.: Modularity and symmetry in computational embryogeny. In: GECCO ’08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp. 1151–1152. ACM, New York, NY, USA (2008). doi: 10.1145/1389095.1389323

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Complex Systems Institute - Paris Ile-de-France (ISC-PIF)CNRSParisFrance
  2. 2.Department of Computer ScienceMemorial UniversityNewfoundlandCanada

Personalised recommendations