A number of generic parameter search functions are available in
scipy.optimize, including simulated annealing. To wrap them in a
scikit-learn interface is fairly trivial. If you are talking about model
selection using simulated annealing, I once wrote a GridSearchCV-like
extension that could use an arbitrary scipy.optimize minimizer over
scikit-learn estimator [hyper] parameters, where continuous real numbers.
Is that the sort of thing you are looking for?
On 23 June 2014 18:19, Michal Romaniuk <michal.romaniu...@imperial.ac.uk>
wrote:
> Has anyone worked on simulated annealing or similar algorithms for
> parameter search?
>
> Michal
>
>
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Quickly connect people, data, and systems into organized workflows
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http://p.sf.net/sfu/Bonitasoft
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