Investigation of Genome Parameters and Sub-Transitions to Guide Evolution of Artificial Cellular Organisms

  • Stefano NicheleEmail author
  • Håkon Hjelde Wold
  • Gunnar Tufte
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8602)


Artificial multi-cellular organisms develop from a single zygote to complex morphologies, following the instructions encoded in their genomes. Small genome mutations can result in very different developed phenotypes. In this paper we investigate how to exploit genotype information in order to guide evolution towards favorable areas of the phenotype solution space, where the sought emergent behavior is more likely to be found. Lambda genome parameter, with its ability to discriminate different developmental behaviors, is incorporated into the fitness function and used as a discriminating factor for genetic distance, to keep resulting phenotype’s developmental behavior close by and encourage beneficial mutations that yield adaptive evolution. Genome activation patterns are detected and grouped into genome parameter sub-transitions. Different sub-transitions are investigated as simple genome parameters, or composed to integrate several genome properties into a more exhaustive composite parameter. The experimental model used herein is based on 2-dimensional cellular automata.


Artificial Development Evolution Complexity Emergence Cellular Automata 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Wagner, A.: Robustness and evolvability: a paradox resolved. Proceedings of the Royal Society B - Biological Sciences 275(1630), 91–100 (2008)CrossRefGoogle Scholar
  2. 2.
    Bar-Yam, Y.: Dynamics of complex systems. Studies in Nonlinearity, p. 864. Westview Press (1997)Google Scholar
  3. 3.
    Miller, J.F.: Evolving developmental programs for adaptation, morphogenesis, and self-repair. In: Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P., Kim, J.T. (eds.) ECAL 2003. LNCS (LNAI), vol. 2801, pp. 256–265. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Tufte, G., Haddow, P.C.: Towards development on a silicon-based cellular computation machine. Natural Computation 4(4), 387–416 (2005)CrossRefzbMATHMathSciNetGoogle Scholar
  5. 5.
    Glover, F., Kochenberg, G.A.: Handbook of metaheuristics. International Series on Operations Research and Management Science, p. 570. Springer (2003)Google Scholar
  6. 6.
    Langton, C.G.: Computation at the edge of chaos: phase transitions and emergant computation. In: Forrest, S. (ed.) Emergent Computation, pp. 12–37. MIT Press (1991)Google Scholar
  7. 7.
    Tufte, G., Nichele, S.: On the correlations between developmental diversity and genomic composition. In: 13th Annual Genetic and Evolutionary Computation Conference, GECCO 2011, pp. 1507–1514. ACM (2011)Google Scholar
  8. 8.
    Nichele, S., Tufte, G.: Genome parameters as information to forecast emergent developmental behaviors. In: Durand-Lose, J., Jonoska, N. (eds.) UCNC 2012. LNCS, vol. 7445, pp. 186–197. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    de Oliveira, G., de Oliveira, P., Omar, N.: Definition and application of a five-parameter characterization of one-dimensional cellular automata rule space. Artificial Life 7, 277–301 (2001)Google Scholar
  10. 10.
    de Oliveira, G., de Oliveira, P., Omar, N.: Guidelines for dynamics-based parameterization of one-dimensional cellular automata rule space. Complexity 6(2) (2001)Google Scholar
  11. 11.
    Kowaliw, T.: Measures of complexity for artificial embryogeny. In: GECCO 2008, pp. 843–850. ACM (2008)Google Scholar
  12. 12.
    Rothlauf, F.: Locality, distance distortion, and binary representations of integers. Working Paper 11/2003. University of MannheimGoogle Scholar
  13. 13.
    Pollard, T.D.: No question about exciting questions in cell biology. PLoS Biol. 11(12), e1001734 (2013). doi: 10.1371/journal.pbio.1001734 CrossRefGoogle Scholar
  14. 14.
    Binder, P.M.: Parametric ordering of complex systems. Physical Review E 49(3), 2023–2025 (1994)CrossRefGoogle Scholar
  15. 15.
    Li, W.: Phenomenology of nonlocal cellular automata. Journal of Statistical Physics 68(5–6), 829–882 (1992)CrossRefzbMATHMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Stefano Nichele
    • 1
    Email author
  • Håkon Hjelde Wold
    • 1
  • Gunnar Tufte
    • 1
  1. 1.Department of Computer and Information ScienceNorwegian University of Science and TechnologyTrondheimNorway

Personalised recommendations