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Duboz et al. 2010

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

Type Journal Article
Author Raphaël Duboz
Author David Versmisse
Author Morgane Travers
Author Eric Ramat
Author Yunne-Jai Shin
URL http://www.sciencedirect.com/science/article/pii/S0304380009008102
Volume 221
Issue 5
Pages 840-849
Publication Ecological Modelling
ISSN 0304-3800
Date March 10, 2010
DOI 10.1016/j.ecolmodel.2009.11.023
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.