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Application of an evolutionary algorithm to the inverse parameter estimation of an individual-based model

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  • Duboz, Raphaël
  • Versmisse, David
  • Travers, Morgane
  • Ramat, Eric
  • Shin, Yunne-Jai

Abstract

Inverse parameter estimation of individual-based models (IBMs) is a research area which is still in its infancy, in a context where conventional statistical methods are not well suited to confront this type of models with data. In this paper, we propose an original evolutionary algorithm which is designed for the calibration of complex IBMs, i.e. characterized by high stochasticity, parameter uncertainty and numerous non-linear interactions between parameters and model output. Our algorithm corresponds to a variant of the population-based incremental learning (PBIL) genetic algorithm, with a specific “optimal individual” operator. The method is presented in detail and applied to the individual-based model OSMOSE. The performance of the algorithm is evaluated and estimated parameters are compared with an independent manual calibration. The results show that automated and convergent methods for inverse parameter estimation are a significant improvement to existing ad hoc methods for the calibration of IBMs.

Suggested Citation

  • Duboz, Raphaël & Versmisse, David & Travers, Morgane & Ramat, Eric & Shin, Yunne-Jai, 2010. "Application of an evolutionary algorithm to the inverse parameter estimation of an individual-based model," Ecological Modelling, Elsevier, vol. 221(5), pages 840-849.
    • Handle: RePEc:eee:ecomod:v:221:y:2010:i:5:p:840-849
    DOI: 10.1016/j.ecolmodel.2009.11.023
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    1. Travers, M. & Shin, Y.-J. & Jennings, S. & Machu, E. & Huggett, J.A. & Field, J.G. & Cury, P.M., 2009. "Two-way coupling versus one-way forcing of plankton and fish models to predict ecosystem changes in the Benguela," Ecological Modelling, Elsevier, vol. 220(21), pages 3089-3099.
    2. Piou, Cyril & Berger, Uta & Grimm, Volker, 2009. "Proposing an information criterion for individual-based models developed in a pattern-oriented modelling framework," Ecological Modelling, Elsevier, vol. 220(17), pages 1957-1967.
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