Biologically inspired computational ecologies: A case study

  • Paul Devine
  • Ray Paton
Evolutionary Approaches to Issues in Biology and Economics
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1305)


Some aspects of evolution are, by their very nature, unsuited to a process of direct experimentation. The work described here is a computational system strongly inspired by real ecology, it is intended as a framework within which the interaction of evolution, learning and cultural effects may be investigated. The design, development and behaviour of the system is outlined in some detail.


Rule Base Asexual Reproduction Rule Discovery Scramble Competition Imitative Learning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [AL91]
    D. Ackley and M. Littman. Interactions between learning and evolution. In Farmer, Langton, Rasmussen, and Taylor, editors, Artificial Life II, pages 488–509, Reading (Mass.), 1991. Addison-Wesley.Google Scholar
  2. [Ba196]
    J. M. Baldwin,A new factor in evolution. The American Naturalist, 30:441–451 and 536–533, 1896.Google Scholar
  3. [Beg85]
    M. Begon. A general theory of life-history variation. In R.M. Silby and R.H. Smith, editors, Behavioural Ecology — Ecological Consequences of Adaptive Behaviour. Blackwell, 1985.Google Scholar
  4. [BGH90]
    L. Booker, D.E. Goldberg, and J.H. Holland. Classifier systems and genetic algorithms. Artificial Intelligence, 1990.Google Scholar
  5. [CL81]
    G. Coughley and J.H. Laughton. Plant-herbivore systems. In R.M. May, editor, Theoretical Ecology, pages 132–166. Blackwell, Oxford, 2 edition, 1981.Google Scholar
  6. [CMB95]
    F. Cecconi, F. Menczer, and R. K. Belew. Maturation and the evolution of imitative learning in artificial organisms. Adaptive Behaviour, 4(1):29–50, 1995.Google Scholar
  7. [CWFL93]
    R. Constanza, L. Wainger, C. Folke, and K. G. M. Ler. Modeling complex ecological economic systems: Toward an evolutionary, dynamic understanding of humans and nature. BioScience, 43:545–555, 1993.Google Scholar
  8. [DC89]
    L. Darden and J. A. Cain. Selection type theories. Philosophy of Science, 56:106–129, 1989.Google Scholar
  9. [DKP96]
    P. Devine, G. Kendall, and R. Paton. When herby met elvis-experiments with genetics based learning systems. In V. J. Rayward-Smith, editor, Modern Heuristic Search Methods, pages 275–292. J. Wiley and Sons, 1996.Google Scholar
  10. [DP97]
    P. Devine and R. C. Paton. Herby, an artificial evolutionary ecology. In Proceeding of ICEC97. IEEE Press, 1997.Google Scholar
  11. [FJ94]
    S. Forrest and T. Jones.Modeling complex adaptive systems with echo. In R. J. Stonier and X. H. Yu, editors, Complex Systems: Mechanisms of Adaptation, pages 3–21. IOS Press, 1994.Google Scholar
  12. [FM94]
    R. French and A. Messinger. Genes, phenes and the baldwin effect. In Artificial Life IV. MIT Press, 1994.Google Scholar
  13. [HDP84]
    M. Huston, D. DeAngelis, and W. Post. New computer models unify ecological theory. Bioscience, 38(10):682–691, 1984.Google Scholar
  14. [Hei94]
    D. Heibeler. The swarm simulation system and individual-based modeling. In Decision Support 2001: Advanced Technology for Natural Resource Management, 1994.Google Scholar
  15. [Ho192]
    J. H. Holland. Adaptation in Natural and Artificial Systems. MIT Press, 1992.Google Scholar
  16. [Jud94]
    O. P. Judson. The rise of the individual-based model in ecology. Trends in Ecology and Evolution, 9:9–14, 1994.Google Scholar
  17. [Lev66]
    R. Levins. The strategy of model building in population biology. American Scientist, 54:421–431, 1966.Google Scholar
  18. [Lom78]
    A. Lomnicki. Individual differences between animals and the natural regulation of their numbers. Journal of Animal Ecology, 47:461–475, 1978.Google Scholar
  19. [MG94]
    J. A. Meyer and A. Guillot. From sab90 to sab94: Four years of animat research. In Cliff, Husbands, Meyer, and Wilson, editors, From animals to animats 3. Proceedings of the third international conference on simulation of adaptive behaviour. MIT Press, 1994.Google Scholar
  20. [Mon67]
    J. Monro. The exploitation and conservation of resources by populations of insects. Journal of Animal Ecology, 36:531–47, 1967.Google Scholar
  21. [Nic54]
    A. J. Nicholson. An outline of the dynamics of animal populations. Australian Journal of Zoology, 2:551–598, 1954.Google Scholar
  22. [Pia74]
    E. R. Pianka. Evolutionary Ecology. Harper and Row, New York, 1974.Google Scholar
  23. [PNC92]
    D. Parisi, S. Nolfi, and F. Cecconi. Learning, behaviour and evolution. In F. J. Varela and P. Bourgine, editors, Towards a practice of autonomous systems, pages 207–216, Cambridge (Mass.), 1992. MIT Press.Google Scholar
  24. [Pri84]
    P. W. Price. Alternative paradigms in community ecology. In P. W. Price, C. N. Slobodchikoff, and W. S. Gaud, editors, A New Ecology, Novel Approaches to Interactive Systems, pages 353–383. J. Wiley and Sons, 1984.Google Scholar
  25. [Sig93]
    K. Sigmund. Games of Life. Oxford University Press, 1993.Google Scholar
  26. [Smi76]
    J. Maynard Smith. Evolution and the theory of games. American Scientist, 64, 1976.Google Scholar
  27. [Wie84]
    J. A. Wiens. Resource systems, populations and communities. In P. W. Price, C. N. Slobodchikoff, and W. S. Gaud, editors, A New Ecology, Novel Approaches to Interactive Systems, pages 397–346. J. Wiley and Sons, 1984.Google Scholar
  28. [Wi190]
    S. W. Wilson. The Animat path to AI. In From Animals to Animats-Proc 1st Int Conf on Simulation of Adaptive Behaviour, 1990.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Paul Devine
    • 1
  • Ray Paton
    • 1
  1. 1.Department of Computer ScienceUniversity of LiverpoolLiverpoolUK

Personalised recommendations