Solution-Locked Averages and Solution-Time Binning in Genetic Programming

  • Riccardo Poli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6021)


Averaging data collected in multiple independent runs across generations is the standard method to study the behaviour of GP. We show that while averaging may represent with good resolution GP’s behaviour in the early stages of a run, it blurs later components of the dynamics. We propose two techniques to improve the situation: solution-locked averaging and solution-time binning. Results indicate that there are significant benefits in adopting these techniques.


Solution Time Symbolic Regression Symbolic Regression Problem Ordinary Average Initial Rapid Growth 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Citi, L., Poli, R., Cinel, C.: High-significance averages of event-related potential via genetic programming. In: Riolo, R.L., O’Reilly, U.-M., McConaghy, T. (eds.) Genetic Programming Theory and Practice VII, Genetic and Evolutionary Computation, May 14-16, vol. 9, pp. 135–157. Springer, Ann Arbor (2009)Google Scholar
  2. 2.
    Hansen, J.C.: Separation of overlapping waveforms having known temporal distributions. Journal of neuroscience methods 9(2), 127–139 (1983)CrossRefGoogle Scholar
  3. 3.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)MATHGoogle Scholar
  4. 4.
    Luck, S.J.: An introduction to the event-related potential technique. MIT Press, Cambridge (2005)Google Scholar
  5. 5.
    Poli, R., Cinel, C., Citi, L., Sepulveda, F.: Reaction-time binning: a simple method for increasing the resolving power of ERP averages. Psychophysiology (Forthcoming, 2010)Google Scholar
  6. 6.
    Poli, R., Langdon, W.B., McPhee, N.F.: A field guide to genetic programming (With contributions by J. R. Koza) (2008),,
  7. 7.
    Zhang, J.: Decomposing stimulus and response component waveforms in ERP. Journal of neuroscience methods 80(1), 49–63 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Riccardo Poli
    • 1
  1. 1.School of Computer Science and Electronic EngineeringUniversity of EssexUK

Personalised recommendations