A new evolutionary diagram: Application to BTGP and information retrieval

  • J. L. Fernández-Villacañas
Engeneering Applications
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1607)


A series of measurements on factors in evolutionary processes are carried out for the application of an evolutionary algorithm, BTGP, to the problem information retrieval. A new evolutionary diagram is proposed that allows us to study the performance of, in principle, any evolutionary algorithm on a task. Taking inspiration from the HR diagram in Astrophysics, algorithms are classified using their degree of variation and the logarithmic ratio of exploitation versus exploration. The latter is measured as the inverse of the mean population fitness versus the fitness variance.


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  1. 1.
    Endler, L., “Natural Selection in the Wild”, Princeton, N.J., Princeton University Press, 1986.Google Scholar
  2. 2.
    Hofbauer, J. and Sigmund, K., “The Theory of Evolution and Dynamical Systems”, Cambridge, Cambridge University Press, 1988.MATHGoogle Scholar
  3. 3.
    Roff, D., “Evolutionary Quantitative Genetics”, London, Chapman and Hall, 1998.Google Scholar
  4. 4.
    Fernández-Villacañas J.L., Marrow, P., Shackleton, M., submitted to GECCO'99, 1999.Google Scholar
  5. 5.
    Bedau, M.A., Snyder, E., Brown, C.T. and Packard, N.H., A comparison of evolutionary activity in artificial evolving systems and in the biosphere, in “Fourth European Conference on Artificial Life”, P. Husbands and I. Harvey (Eds.), pp. 125–134, Cambridge, MA. MIt Press, 1997.Google Scholar
  6. 6.
    Altenberg, L., The evolution of Evolvability in genetic programming, in “Advances in Generic Programming”, K.E. Kinnear Jr. (Ed.), pp. 47–74, Cambridge, MA, MIT Press, 1994.Google Scholar
  7. 7.
    Wagner, G.P., Altenberg, L., Complex adaptations and the evolution of evolvability, “Evolution” 50, 967–976, 1996.CrossRefGoogle Scholar
  8. 8.
    Falconer, D.S., “Introduction to Quantitative Genetics”, 3rd. ed. Harlow. Longman, 1994.Google Scholar
  9. 9.
    Mühlenbein, H., The equation for the response to selection and its use for prediction, ”Evolutionary Computation” 5, 303–346, 1998.Google Scholar
  10. 10.
    Hordijk, W., A measure of landscapes, “Evolutionary Computation” 4, 335–360, 1997.Google Scholar
  11. 11.
    Bedau, M.A. and Packard, N.H., Measurement of evolutionary activity, teleology and life, “Artificial Life II”, C.G. Langton, C. Taylor, J.D. Farmer and S. Rasmussen (Eds.), pp. 431–461, Redwood City, CA, Addison-Wesley, 1991.Google Scholar
  12. 12.
    Mitchell, M. and Forrest, S., Genetic algorithms and Artificial Life, “Artificial Life” 1, 267–289, 1995.CrossRefGoogle Scholar
  13. 13.
    Fernández-Villacañas, J.L. and Exell, J., BTGP and information retrieval, in “Proceedings of the Second International Conference ACEDC'96”, PEDC, University of Plymouth, 1996.Google Scholar
  14. 14.
    Hertzzrpung, E., Ueber die Verwendung photographischer effektiver Wellenlaengen zur Bestimmung von Farbenaequivalenten, “Publikationen des Astrophysikalischen Observatoriums zu Potsdam”, 22. Bd., 1. Nr. 63, 1911.Google Scholar
  15. 15.
    Russell, H.N., Nature, no. 93, 252, 1914.Google Scholar

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© Springer-Verlag Berlin Heidelberg 1999

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  • J. L. Fernández-Villacañas

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