Duboz R. auteur aut IRD Versmisse D. auteur aut IRD Travers M. auteur aut IRD Ramat E. auteur aut IRD Shin Yunne-Jai auteur aut IRD text journalArticle eng text/pdf born digital access 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. specialized Parameter estimation Model calibration Evolutionary and genetic algorithms Individual-based model Marine ecosystem model 020 036 221 5 840-849 2010 0304-3800 https://www.documentation.ird.fr/hor/fdi:010049333 10.1016/j.ecolmodel.2009.11.023 0304-3800 [F B010049333] https://www.documentation.ird.fr/hor/fdi:010049333 https://www.documentation.ird.fr/intranet/publi/2010/03/010049333.pdf Accès réservé (Intranet de l'IRD) IRD - Base Horizon / Pleins textes 2010-04-06 2017-08-23 fdi:010049333 fre