Evolving Interpolating Models of Net Ecosystem CO2 Exchange Using Grammatical Evolution

  • Miguel Nicolau
  • Matthew Saunders
  • Michael O’Neill
  • Bruce Osborne
  • Anthony Brabazon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7244)


Accurate measurements of Net Ecosystem Exchange of CO 2 between atmosphere and biosphere are required in order to estimate annual carbon budgets. These are typically obtained with Eddy Covariance techniques. Unfortunately, these techniques are often both noisy and incomplete, due to data loss through equipment failure and routine maintenance, and require gap-filling techniques in order to provide accurate annual budgets. In this study, a grammar-based version of Genetic Programming is employed to generate interpolating models for flux data. The evolved models are robust, and their symbolic nature provides further understanding of the environmental variables involved.


Grammatical evolution Real-world applications Symbolic regression 


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  1. 1.
    Moffat, A., et al.: Comprehensive comparison of gap-filling techniques for eddy covariance net carbon fluxes. Agricultural and Forest Meteorology 147, 209–232 (2007)CrossRefGoogle Scholar
  2. 2.
    Azad, R.M.A., Ansari, A.R., Ryan, C., Walsh, M., McGloughlin, T.: An evolutionary approach to wall shear stress prediction in a grafted artery. Applied Soft Computing 4(2), 139–148 (2004)CrossRefGoogle Scholar
  3. 3.
    Brabazon, A., O’Neill, M.: Biologically Inspired Algorithms for Financial Modelling. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  4. 4.
    Falge, E., et al.: Gap filling strategies for defensible annual sums of net ecosystem exchange. Agricultural and Forest Meteorology 107, 43–69 (2001)CrossRefGoogle Scholar
  5. 5.
    Gagné, C., Schoenauer, M., Parizeau, M., Tomassini, M.: Genetic Programming, Validation Sets, and Parsimony Pressure. In: Collet, P., Tomassini, M., Ebner, M., Gustafson, S., Ekárt, A. (eds.) EuroGP 2006. LNCS, vol. 3905, pp. 109–120. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Goulden, M., Munger, W., Fan, S.M., Daube, B., Wofsy, S.: Measurements of carbon sequestration by long-term eddy covariance: methods and critical evaluation of accuracy. Global Change Biology 2, 169–182 (1996)CrossRefGoogle Scholar
  7. 7.
    Harper, R.: GE, explosive grammars and the lasting legacy of bad initialisation. In: Proceedings of IEEE Congress on Evolutionary Computation, CEC 2010, July 18-23, Barcelona, Spain, pp. 2602–2609. IEEE Press (2010)Google Scholar
  8. 8.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press (1975)Google Scholar
  9. 9.
    Hui, D., Wan, S., Su, B., Katul, G., Monson, R., Luo, Y.: Gap-filling missing data in eddy covariance measurements using multiple imputation (mi) for annual estimates. Agricultural and Forest Meteorology 121, 93–111 (2004)CrossRefGoogle Scholar
  10. 10.
    Humphreys, E., Black, T.A., Morgenstern, K., Cai, T., Drewitt, G., Nesic, Z., Trofymow, J.: Carbon dioxide fluxes in coastal douglas-fir stands at different stages of development after clearcut harvesting. Agricultural and Forest Meteorology 140, 6–22 (2006)CrossRefGoogle Scholar
  11. 11.
    Keijzer, M.: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E.P.K., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, pp. 70–82. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  12. 12.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press (1992)Google Scholar
  13. 13.
    Reichstein, M., et al.: On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Global Change Biology 11, 1424–1439 (2005)CrossRefGoogle Scholar
  14. 14.
    McKay, R.I., Nguyen, X.H., Whigham, P.A., Shan, Y., O’Neill, M.: Grammar-based genetic programming - a survey. Genetic Programming and Evolvable Machines 11(3-4), 365–396 (2010)CrossRefGoogle Scholar
  15. 15.
    Murphy, J.E., O’Neill, M., Carr, H.: Exploring Grammatical Evolution for Horse Gait Optimisation. In: Vanneschi, L., Gustafson, S., Moraglio, A., De Falco, I., Ebner, M. (eds.) EuroGP 2009. LNCS, vol. 5481, pp. 183–194. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  16. 16.
    Nicolau, M.: Automatic grammar complexity reduction in grammatical evolution. In: Poli, R., et al. (eds.) Genetic and Evolutionary Computation Conference (GECCO) Workshops. AAAI (2004)Google Scholar
  17. 17.
    O’Neill, M., Ryan, C.: Grammatical Evolution - Evolutionary Automatic Programming in an Arbitrary Language. Genetic Programming, vol. 4. Kluwer Academic (2003)Google Scholar
  18. 18.
    Papale, D., Valentini, R.: A new assessment of european forests carbon exchanges by eddy fluxes and artificial neural network spatialization. Global Change Biology 9, 525–535 (2003)CrossRefGoogle Scholar
  19. 19.
    Perez, D., Nicolau, M., O’Neill, M., Brabazon, A., Yannakakis, G.N.: Evolving Behaviour Trees for the Mario AI Competition Using Grammatical Evolution. In: Di Chio, C., Cagnoni, S., Cotta, C., Ebner, M., Ekárt, A., Esparcia-Alcázar, A.I., Merelo, J.J., Neri, F., Pruess, M., Richter, H., Togelius, J., Yannakakis, G.N. (eds.) EvoApplications 2011, Part I. LNCS, vol. 6624, pp. 123–132. Springer, Heidelberg (2011)Google Scholar
  20. 20.
    Pingintha, N., Leclerc, M., Beasley, J., Durden, D., Zhang, G., Senthong, C., Rowland, D.: Hysteresis response of daytime net ecosystem exchange during drought. Biogeosciences 7, 1159–1170 (2010)CrossRefGoogle Scholar
  21. 21.
    Ryan, C., Azad, A.: Sensible initialisation in grammatical evolution. In: Cantú-Paz, E., et al. (eds.) Genetic and Evolutionary Computation Conference (GECCO) Workshops. AAAI (2003)Google Scholar
  22. 22.
    Ryan, C., Collins, J., O’Neill, M.: Grammatical Evolution: Evolving Programs for an Arbitrary Language. In: Banzhaf, W., Poli, R., Schoenauer, M., Fogarty, T.C. (eds.) EuroGP 1998. LNCS, vol. 1391, pp. 83–95. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  23. 23.
    Tuite, C., Agapitos, A., O’Neill, M., Brabazon, A.: A Preliminary Investigation of Overfitting in Evolutionary Driven Model Induction: Implications for Financial Modelling. In: Di Chio, C., Brabazon, A., Di Caro, G.A., Drechsler, R., Farooq, M., Grahl, J., Greenfield, G., Prins, C., Romero, J., Squillero, G., Tarantino, E., Tettamanzi, A.G.B., Urquhart, N., Uyar, A.Ş. (eds.) EvoApplications 2011, Part II. LNCS, vol. 6625, pp. 120–130. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Miguel Nicolau
    • 1
  • Matthew Saunders
    • 2
  • Michael O’Neill
    • 1
  • Bruce Osborne
    • 2
  • Anthony Brabazon
    • 1
  1. 1.Natural Computing Research & Applications GroupUniversity College DublinDublinIreland
  2. 2.UCD School of Biology and Evironmental ScienceUniversity College DublinDublinIreland

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