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Half a Billion Simulations: Evolutionary Algorithms and Distributed Computing for Calibrating the Simpoplocal Geographical Model

Author

Listed:
  • Clara Schmitt
  • Sébastien Rey-Coyrehourcq
  • Romain Reuillon

    (UMR Géographie-Cités, 13 rue du Four 75006 Paris, France)

  • Denise Pumain

    (Université Paris 1 Panthéon-Sorbonne, 12 Place du Panthéon, 75005 Paris, France)

Abstract

Multiagent geographical models integrate very large numbers of spatial interactions. In order to validate these models a large amount of computing is necessary for their simulation and calibration. Here a new data-processing chain, including an automated calibration procedure, is tested on a computational grid using evolutionary algorithms. This is applied for the first time to a geographical model designed to simulate the evolution of an early urban settlement system. The method enables us to reduce the computing time and provides robust results. Using this method, we identify several parameter settings that minimize three objective functions that quantify how closely the model results match a reference pattern. As the values of each parameter in different settings are very close, this estimation considerably reduces the initial possible domain of variation of the parameters. Thus the model is a useful tool for further multiple applications in empirical historical situations.

Suggested Citation

  • 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.
    • Handle: RePEc:sae:envirb:v:42:y:2015:i:2:p:300-315
    DOI: 10.1068/b130064p
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    References listed on IDEAS

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