Evolvability of the Genotype-Phenotype Relation in Populations of Self-Replicating Digital Organisms in a Tierra-Like System

  • Attila Egri-Nagy
  • Chrystopher L. Nehaniv
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2801)


In other Tierra-like systems the genotype is a sequence of instructions and the phenotype is the corresponding executed algorithm. This way the genotype-phenotype mapping is constrained by the structure of a creature’s processor, and this structure was fixed for an evolutionary scenario in previous systems. Our approach here is to put the mapping under evolutionary control. We use a universal processor (analogous to a universal Turing-machine) and put the structural description of the creature’s processor as well as the instruction set of the actual processor into the organism’s genome. The life-cycle of an organism begins with building its actual processor, then the organism can start executing instructions in the rest of its genome with the newly built processor. Since the definitions of the processors and instruction sets are in the genome, they are subject to mutations and heritable variation enabling their evolution. In this work we investigate the evolutionary development of the processor structures. In evolving populations, changes in the components (registers, stacks, queues), variations in instruction-set size and the redefinition of the instructions can be observed during experiments.


Descriptive Part Executable Code Gestation Time Replication Algorithm Actual Processor 
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  1. 1.
    Avida. Digital Life Laboratory,
  2. 2.
  3. 3.
    Adami, C., Titus Brown, C.: Evolutionary learning in the 2D artificial life system “Avida”. In: Proc. Artificial Life IV, pp. 377–381. MIT Press, Cambridge (1994)Google Scholar
  4. 4.
    Adami, C., Wilke, C.O.: The biology of digital organisms. Trends in Ecology & Evolution 17(11), 528–532 (2002)CrossRefGoogle Scholar
  5. 5.
    Marc de Groot. Primordial soup (unpublished)Google Scholar
  6. 6.
    Nehaniv, C.L. (ed.): BioSystems, special issue on evolvability, vol. 69(2-3) (2003)Google Scholar
  7. 7.
    Gould, S.J., Eldredge, N.: Punctuated equilibrium comes of age. Nature 366, 223–227 (1993)CrossRefGoogle Scholar
  8. 8.
    Kirschner, M., Gerhart, J.: Evolvability. PNAS 95, 8420–8427 (1998)CrossRefGoogle Scholar
  9. 9.
    Klaiber, A.: The technology behind the Crusoe processors (2000),
  10. 10.
    Ofria, C., Adami, C., Collier, T.C.: Design of evolvable computer languages. IEEE Transactions on Evolutionary Computation 6(4), 420–424 (2002)CrossRefGoogle Scholar
  11. 11.
    Ray, T.S.: An approach to the synthesis of life. In: Artificial Life II. Studies in the Sciences of Complexity, vol. IX, pp. 371–408. Addison Wesley, Reading (1992)Google Scholar
  12. 12.
    Ray, T.S.: Evolution, complexity, entropy, and artificial reality. Physica D 75, 239–263 (1994)zbMATHCrossRefGoogle Scholar
  13. 13.
    Maynard Smith, J., Szathmáry, E.: The Major Transitions in Evolution. W.H. Freeman, New York (1995)Google Scholar
  14. 14.
    Tabak, D.: RISC systems and applications. Research Studies Press, Hertfordshire (1996)Google Scholar
  15. 15.
    Taylor, T.: Some representational and ecological aspects of evolvability. In: Maley, C.C., Boudreau, E. (eds.) Artificial Life 7 Workshop Proceedings, pp. 35–38 (2000), Available online at:
  16. 16.
    Turing, A.: On computable numbers, with an application to the Entscheidungsproblem. Proceedings of the London Mathematical Society 42, 230–265 (1936)zbMATHCrossRefGoogle Scholar
  17. 17.
    Wagner, G.P., Altenberg, L.: Complex adaptations and the evolution of evolvability. Evolution 50(3), 967–976 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Attila Egri-Nagy
    • 1
  • Chrystopher L. Nehaniv
    • 1
  1. 1.Department of Computer Science, Faculty of Engineering and Information SciencesUniversity of HertfordshireHatfield, HertfordshireUnited Kingdom

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