@article{fdi:010049333,
  title = {{A}pplication of an evolutionary algorithm to the inverse parameter estimation of an individual-based model},
  author = {{D}uboz, {R}. and {V}ersmisse, {D}. and {T}ravers, {M}. and {R}amat, {E}. and {S}hin, {Y}unne-{J}ai},
  editor = {},
  language = {{ENG}},
  abstract = {{I}nverse parameter estimation of individual-based models ({IBM}s) 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. {I}n this paper, we propose an original evolutionary algorithm which is designed for the calibration of complex {IBM}s, i.e. characterized by high stochasticity, parameter uncertainty and numerous non-linear interactions between parameters and model output. {O}ur algorithm corresponds to a variant of the population-based incremental learning ({PBIL}) genetic algorithm, with a specific "optimal individual" operator. {T}he method is presented in detail and applied to the individual-based model {OSMOSE}. {T}he performance of the algorithm is evaluated and estimated parameters are compared with an independent manual calibration. {T}he results show that automated and convergent methods for inverse parameter estimation are a significant improvement to existing ad hoc methods for the calibration of {IBM}s.},
  keywords = {{P}arameter estimation ; {M}odel calibration ; {E}volutionary and genetic algorithms ; {I}ndividual-based model ; {M}arine ecosystem model},
  booktitle = {},
  journal = {{E}cological {M}odelling},
  volume = {221},
  numero = {5},
  pages = {840--849},
  ISSN = {0304-3800},
  year = {2010},
  DOI = {10.1016/j.ecolmodel.2009.11.023},
  URL = {https://www.documentation.ird.fr/hor/fdi:010049333},
}