IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v221y2010i5p840-849.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380009008102
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2009.11.023?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Clara Schmitt & Sébastien Rey-Coyrehourcq & Romain Reuillon & Denise Pumain, 2015. "Half a Billion Simulations: Evolutionary Algorithms and Distributed Computing for Calibrating the Simpoplocal Geographical Model," Environment and Planning B, , vol. 42(2), pages 300-315, April.
    2. Hill Cruz, Mariana & Frenger, Ivy & Getzlaff, Julia & Kriest, Iris & Xue, Tianfei & Shin, Yunne-Jai, 2022. "Understanding the drivers of fish variability in an end-to-end model of the Northern Humboldt Current System," Ecological Modelling, Elsevier, vol. 472(C).
    3. Haythorne, Sean & Skabar, Andrew, 2013. "An improved pattern-guided evolution approach for the development of adaptive individual-based ecological models," Ecological Modelling, Elsevier, vol. 252(C), pages 72-82.
    4. Watkins, Katherine Shepard & Rose, Kenneth A., 2013. "Evaluating the performance of individual-based animal movement models in novel environments," Ecological Modelling, Elsevier, vol. 250(C), pages 214-234.
    5. Bracis, Chloe & Lehuta, Sigrid & Savina-Rolland, Marie & Travers-Trolet, Morgane & Girardin, Raphaël, 2020. "Improving confidence in complex ecosystem models: The sensitivity analysis of an Atlantis ecosystem model," Ecological Modelling, Elsevier, vol. 431(C).
    6. Xing, Lei & Zhang, Chongliang & Chen, Yong & Shin, Yunne-Jai & Verley, Philippe & Yu, Haiqing & Ren, Yiping, 2017. "An individual-based model for simulating the ecosystem dynamics of Jiaozhou Bay, China," Ecological Modelling, Elsevier, vol. 360(C), pages 120-131.
    7. Marzloff, Martin P. & Johnson, Craig R. & Little, L. Rich & Soulié, Jean-Christophe & Ling, Scott D. & Frusher, Stewart D., 2013. "Sensitivity analysis and pattern-oriented validation of TRITON, a model with alternative community states: Insights on temperate rocky reefs dynamics," Ecological Modelling, Elsevier, vol. 258(C), pages 16-32.
    8. Guichard, S. & Kriticos, D.J. & Kean, J.M. & Worner, S.P., 2010. "Modelling pheromone anemotaxis for biosecurity surveillance: Moth movement patterns reveal a downwind component of anemotaxis," Ecological Modelling, Elsevier, vol. 221(23), pages 2801-2807.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Grimm, Volker & Berger, Uta, 2016. "Robustness analysis: Deconstructing computational models for ecological theory and applications," Ecological Modelling, Elsevier, vol. 326(C), pages 162-167.
    2. Grimm, Volker & Berger, Uta, 2016. "Structural realism, emergence, and predictions in next-generation ecological modelling: Synthesis from a special issue," Ecological Modelling, Elsevier, vol. 326(C), pages 177-187.
    3. Caracappa, Joseph C. & Beet, Andrew & Gaichas, Sarah & Gamble, Robert J. & Hyde, Kimberly J.W. & Large, Scott I. & Morse, Ryan E. & Stock, Charles A. & Saba, Vincent S., 2022. "A northeast United States Atlantis marine ecosystem model with ocean reanalysis and ocean color forcing," Ecological Modelling, Elsevier, vol. 471(C).
    4. Kumar, Vijay & Kumari, Beena, 2015. "Mathematical modelling of the seasonal variability of plankton and forage fish in the Gulf of Kachchh," Ecological Modelling, Elsevier, vol. 313(C), pages 237-250.
    5. Xing, Lei & Zhang, Chongliang & Chen, Yong & Shin, Yunne-Jai & Verley, Philippe & Yu, Haiqing & Ren, Yiping, 2017. "An individual-based model for simulating the ecosystem dynamics of Jiaozhou Bay, China," Ecological Modelling, Elsevier, vol. 360(C), pages 120-131.
    6. Grüss, Arnaud & Palomares, Maria L.D. & Poelen, Jorrit H. & Barile, Josephine R. & Aldemita, Casey D. & Ortiz, Shelumiel R. & Barrier, Nicolas & Shin, Yunne-Jai & Simons, James & Pauly, Daniel, 2019. "Building bridges between global information systems on marine organisms and ecosystem models," Ecological Modelling, Elsevier, vol. 398(C), pages 1-19.
    7. Jager, Henriette I. & DeAngelis, Donald L., 2018. "The confluences of ideas leading to, and the flow of ideas emerging from, individual-based modeling of riverine fishes," Ecological Modelling, Elsevier, vol. 384(C), pages 341-352.
    8. Oladayo Amed Idris & Prosper Opute & Israel Ropo Orimoloye & Mark Steve Maboeta, 2022. "Climate Change in Africa and Vegetation Response: A Bibliometric and Spatially Based Information Assessment," Sustainability, MDPI, vol. 14(9), pages 1-19, April.
    9. Diaz, Frédéric & Bănaru, Daniela & Verley, Philippe & Shin, Yunne-Jai, 2019. "Implementation of an end-to-end model of the Gulf of Lions ecosystem (NW Mediterranean Sea). II. Investigating the effects of high trophic levels on nutrients and plankton dynamics and associated feed," Ecological Modelling, Elsevier, vol. 405(C), pages 51-68.
    10. Bănaru, Daniela & Diaz, Fréderic & Verley, Philippe & Campbell, Rose & Navarro, Jonathan & Yohia, Christophe & Oliveros-Ramos, Ricardo & Mellon-Duval, Capucine & Shin, Yunne-Jai, 2019. "Implementation of an end-to-end model of the Gulf of Lions ecosystem (NW Mediterranean Sea). I. Parameterization, calibration and evaluation," Ecological Modelling, Elsevier, vol. 401(C), pages 1-19.
    11. Juste Raimbault & Julien Perret, 2019. "Generating urban morphologies at large scales," Post-Print halshs-02265415, HAL.
    12. Maar, Marie & Butenschön, Momme & Daewel, Ute & Eggert, Anja & Fan, Wei & Hjøllo, Solfrid S. & Hufnagl, Marc & Huret, Martin & Ji, Rubao & Lacroix, Geneviève & Peck, Myron A. & Radtke, Hagen & Sailley, 2018. "Responses of summer phytoplankton biomass to changes in top-down forcing: Insights from comparative modelling," Ecological Modelling, Elsevier, vol. 376(C), pages 54-67.
    13. Libralato, Simone & Solidoro, Cosimo, 2009. "Bridging biogeochemical and food web models for an End-to-End representation of marine ecosystem dynamics: The Venice lagoon case study," Ecological Modelling, Elsevier, vol. 220(21), pages 2960-2971.
    14. Bauduin, Sarah & McIntire, Eliot & St-Laurent, Martin-Hugues & Cumming, Steve, 2016. "Overcoming challenges of sparse telemetry data to estimate caribou movement," Ecological Modelling, Elsevier, vol. 335(C), pages 24-34.
    15. Byer, Nathan W. & Reid, Brendan N., 2022. "The emergence of imperfect philopatry and fidelity in spatially and temporally heterogeneous environments," Ecological Modelling, Elsevier, vol. 468(C).
    16. Jakoby, Oliver & Grimm, Volker & Frank, Karin, 2014. "Pattern-oriented parameterization of general models for ecological application: Towards realistic evaluations of management approaches," Ecological Modelling, Elsevier, vol. 275(C), pages 78-88.
    17. Travers-Trolet, Morgane & Coppin, Franck & Cresson, Pierre & Cugier, Philippe & Oliveros-Ramos, Ricardo & Verley, Philippe, 2019. "Emergence of negative trophic level-size relationships from a size-based, individual-based multispecies fish model," Ecological Modelling, Elsevier, vol. 410(C), pages 1-1.
    18. Fu, Caihong & Travers-Trolet, Morgane & Velez, Laure & Grüss, Arnaud & Bundy, Alida & Shannon, Lynne J. & Fulton, Elizabeth A. & Akoglu, Ekin & Houle, Jennifer E. & Coll, Marta & Verley, Philippe & He, 2018. "Risky business: The combined effects of fishing and changes in primary productivity on fish communities," Ecological Modelling, Elsevier, vol. 368(C), pages 265-276.
    19. Peters, Ronny & Lin, Yue & Berger, Uta, 2016. "Machine learning meets individual-based modelling: Self-organising feature maps for the analysis of below-ground competition among plants," Ecological Modelling, Elsevier, vol. 326(C), pages 142-151.
    20. Piou, Cyril & Prévost, Etienne, 2012. "A demo-genetic individual-based model for Atlantic salmon populations: Model structure, parameterization and sensitivity," Ecological Modelling, Elsevier, vol. 231(C), pages 37-52.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:221:y:2010:i:5:p:840-849. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.