Skip to main content
SpringerLink
Log in

Tracking the red queen: Measurements of adaptive progress in co-evolutionary simulations

  • 2. Origins of Life and Evolution
  • Conference paper
  • First Online:
Advances in Artificial Life (ECAL 1995)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 929))

Included in the following conference series:

Abstract

Co-evolution can give rise to the “Red Queen effect”, where interacting populations alter each other's fitness landscapes. The Red Queen effect significantly complicates any measurement of co-evolutionary progress, introducing fitness ambiguities where improvements in performance of co-evolved individuals can appear as a decline or stasis in the usual measures of evolutionary progress. Unfortunately, no appropriate measures of fitness given the Red Queen effect have been developed in artificial life, theoretical biology, population dynamics, or evolutionary genetics. We propose a set of appropriate performance measures based on both genetic and behavioral data, and illustrate their use in a simulation of co-evolution between genetically specified continuous-time noisy recurrent neural networks which generate pursuit and evasion behaviors in autonomous agents.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. M. Bedau and N. Packard. Measurement of evolutionary activity, teleology, and life. In C. Langton, C. Taylor, J. D. Farmer, and S. Rasmussen, eds, Artificial Life II, pp.431–461. Addison Wesley, 1992.

    Google Scholar 

  2. D. Cliff, I. Harvey, P. Husbands. Explorations in evolutionary robotics. Adapt. Behav., 2(1):71–108, 1993.

    Google Scholar 

  3. D. Cliff and G. F. Miller. Co-evolution of pursuit and evasion II: simulation methods and results. COGS Technical Report CSRP377, University of Sussex, 1995.

    Google Scholar 

  4. D. Cliff and G. F. Miller. Tracking the Red Queen: Measurements of coevolutionary progress in open-ended simulations. COGS Technical Report CSRP363, University of Sussex, 1995.

    Google Scholar 

  5. R. Dawkins. The Blind Watchmaker. Longman, Essex, 1986.

    Google Scholar 

  6. N. Eldredge. Macroevolutionary dynamics: Species, niches, and adaptive peaks. McGraw-Hill, 1989.

    Google Scholar 

  7. D. J. Futuyama and M. Slatkin, editors. Coevolution. Sinauer, 1983.

    Google Scholar 

  8. R. C. Gonzalez and P. Wintz. Digital Image Processing. Addison-Wesley, 1977.

    Google Scholar 

  9. S. J. Gould. Wonderful Life: The Burgess Shale and the Nature of History. Penguin, 1989.

    Google Scholar 

  10. S. Kauffman. The Origins of Order: Self-Organization and Selection in Evolution. OUP, 1993.

    Google Scholar 

  11. J. R. Krebs and N. B. Davies. An Introduction to Behaviuoral Ecology. Blackwell Scientific, 1993.

    Google Scholar 

  12. G. F. Miller and D. Cliff. Co-evolution of pursuit and evasion I: Biological and game-theoretic foundations. Technical Report CSRP311, University of Sussex School of Cognitive and Computing Sciences, 1994.

    Google Scholar 

  13. G. F. Miller and D. Cliff. Protean behavior in dynamic games: Arguments for the co-evolution of pursuit-evasion tactics. In D. Cliff, P. Husbands, J.-A. Meyer, and S. Wilson, editors, Proc. Third Int. Conf. Simulation Adaptive Behavior (SAB94), pages 411–420. M.I.T. Press Bradford Books, 1994.

    Google Scholar 

  14. E. Renshaw. Modelling Biological Populations in Space and Time. Cambridge University Press, 1991.

    Google Scholar 

  15. C. Reynolds. Competition, coevolution, and the game of tag. In R. Brooks and P. Maes, editors, Artificial Life IV, pages 59–69. M.I.T. Press Bradford Books, 1994.

    Google Scholar 

  16. M. Ridley. The Red Queen: Sex and the evolution of human nature. Viking, London, 1993.

    Google Scholar 

  17. J. Segers and W. D. Hamilton. Parasites and sex. In R. E. Michod and B. R. Levin, editors, The evolution of sex: some current, ideas, pages 176–193. Sinauer, Sunderland, MA, 1988.

    Google Scholar 

  18. K. Sims. Evolving 3D morphology and behavior by competition. In R. Brooks and P. Maes, editors, Artificial Life IV, pages 28–39. M.I.T. Press Bradford Books, 1994.

    Google Scholar 

  19. L. van Valen. A new evolutionary law. Evolutionary Theory, 1:1–30, 1973.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Cognitive and Computing Sciences, University of Sussex, BN1 9QH, Brighton, UK

    Dave Cliff

  2. Department of Psychology, University of Nottingham, NG7 2RD, Nottingham, UK

    Geoffrey F. Miller

Authors
  1. Dave Cliff
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Geoffrey F. Miller
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Federico Morán Alvaro Moreno Juan Julián Merelo Pablo Chacón

Rights and permissions

Reprints and permissions

Copyright information

© 1995 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Cliff, D., Miller, G.F. (1995). Tracking the red queen: Measurements of adaptive progress in co-evolutionary simulations. In: Morán, F., Moreno, A., Merelo, J.J., Chacón, P. (eds) Advances in Artificial Life. ECAL 1995. Lecture Notes in Computer Science, vol 929. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-59496-5_300

Download citation

  • DOI: https://doi.org/10.1007/3-540-59496-5_300

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-59496-3

  • Online ISBN: 978-3-540-49286-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation