Institut für Astronomie und Astrophysik
Abteilung AstronomieSand 1, D-72076 Tübingen, Germany
perform genetic algorithm fit process of given model to data
quiet - Do not show fit processing. selected - Apply fit to selected data points only. nodata - Take also models into account which are in groups containing no data. May be usefull when building complex models refering one to other. nosave - Do not save intermediate population parameters in separate group. The default is false, i.e. save the data. cont - Continue with population saved last run (if nosave is not set).
All inputs/options may be set with the "set" command. The command prefix is "gafit". iternum - Number of iterations to perform. Default: 100. popnum - Size of the population. Default: 100 childnum - Number of children of next generation. Must be >=2. Default: 3 mutrate - Mutation rate. That is the probability that a mutation occurs. Default: 0.01 selpresaure - Factor defining the selection pressure during each run. 0 means no pressure at all (selection is done pure randomly and should not be used), 1 means equal random/fittness selection. Higher values increase influence of fittness. Default: 0.5 (more randomness but existing selective pressure). twinmutrate - Mutation rate to change equal individuums with. Default: 0.5 elitism_num - Number of best-fit individuums to keep and not overwrite/mutate. Default: 1. maxchange - Maximal number chi^2 does not change till stop. testnum - Set this number to values > 1 to run the fitting more than once to get more confidence of the results. Best fit value will be reported. iterplot - Plot for each population current plot with best fit parameters. This plot might represent intermediate results. iterplotout - Try to plot out intermediate results.
The gafit command performs genetic algorithm fit process resembling the one being described in Charbonneau, 1995. Here a much simpler verison is used which performs following steps: 1.) create population consisting of individuums who are described by parameters which are the fit parameters (the genotype). The phaenotype is the model function value which has to be fit to the data (the environment). 2.) Each population is randomly separated into two parents which are to be maried. 3.) Some (>2) children are generated from the parents which inherit the parameter from either mother/father (randomly). 4.) With a certain mutation rate some parameters are changed. 5.) All individuums of the next generation are tested for the environment (=data). Best matching one are kept. 6.) Individuums which have the same parameters as other will be deposited with an additional mutation. 7.) Repeat that till either the best fit value does not change for more that certain iterations or chi_red is less than 1. It is important to double-side limit all relevant parameters, otherwise an error is reported. Use the "limit"/"show,limit" command to do that. During fitting parameter handling is as follows: - if a parameter is frozen (fixed flag = 1) the parameter will not be touched while fitting. - tied parameters are not supported currently. - groups which contain a model but no valid data points are ignored. In case of the selected flag (s.a.) only selected data points are taken into account, i.e. if no data points are selected the group is ignored. - groups which contain valid data points but no model are ignored.
Changes parameter values/errors in environment according fit result.
Long lasting fit processes may be interrupted with "Q". This task may take its cpu!
> model, ... > gafit -> fit result > plot,data+model,res
$Id: cafe_gafit.pro,v 1.20 2004/11/01 16:19:29 goehler Exp $
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