Difference between revisions of "Optimisation mode"

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where, C is the cost function value, M is the measured value, m is the model predicted value, n is the normalisation constant, and the suffix i, j, k, refers to the different optimisation datasets.  
 
where, C is the cost function value, M is the measured value, m is the model predicted value, n is the normalisation constant, and the suffix i, j, k, refers to the different optimisation datasets.  
  
To run in this mode, the subroutine [[Creategadriver]] is called to create the Ga.inp file, which takes the parameters that are going to be optimised and puts them in a format that the genetic algorithm solver can read.  
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To run in this mode, the subroutine '''Creategadriver''' is called to create the Ga.inp file, which takes the parameters that are going to be optimised and puts them in a format that the genetic algorithm solver can read.  
The genetic algorithm solver is run from the routine [[Gafortran]]. In this routine a set of parameter values are generated with respect to the initial parameter values, for the first run, and after that they are set to the ‘fittest’ parameters as determined by the genetic algorithm solver. The parameters supplied by Gafortran the main generic glacier model, [[Valley]], is called.
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The genetic algorithm solver is run from the routine '''Gafortran'''. In this routine a set of parameter values are generated with respect to the initial parameter values, for the first run, and after that they are set to the ‘fittest’ parameters as determined by the genetic algorithm solver. The parameters supplied by Gafortran are used in the main generic glacier model, Valley, described in '''[[Model Structure]]'''.

Latest revision as of 17:48, 16 November 2007

Optimisation mode

In this mode, the genetic algorithm solver is employed to determine the best values for the free parameters so that the difference between the modelled and the observed glacier response to climatic forcing is minimized. The cost function is determined by,

[math]\displaystyle{ C = \frac{1}{n_{i}}\sum_{i} \left|{M_{i}-m_{i}}\right|+\frac{1}{n_{j}}\sum_{j} \left|{M_{j}-m_{j}}\right|+\frac{1}{n_{k}}\sum_{k} \left|{M_{k}-m_{k}}\right|+... }[/math]

where, C is the cost function value, M is the measured value, m is the model predicted value, n is the normalisation constant, and the suffix i, j, k, refers to the different optimisation datasets.

To run in this mode, the subroutine Creategadriver is called to create the Ga.inp file, which takes the parameters that are going to be optimised and puts them in a format that the genetic algorithm solver can read. The genetic algorithm solver is run from the routine Gafortran. In this routine a set of parameter values are generated with respect to the initial parameter values, for the first run, and after that they are set to the ‘fittest’ parameters as determined by the genetic algorithm solver. The parameters supplied by Gafortran are used in the main generic glacier model, Valley, described in Model Structure.