[SciPy-Dev] Consideration of differential evolution minimizer being added to scipy.optimize.

[Ralf Gommers]

On Tue, Mar 4, 2014 at 3:21 AM, Andrew Nelson <andyfaff at gmail.com> wrote:

> I have written some code implementing the differential evolution
> minimization algorithm, as invented by Storn and Price.  It's a
> stochastic technique, not gradient based, but it's quite good at
> finding global minima of functions.
>
> (see http://www1.icsi.berkeley.edu/~storn/code.html,
> http://en.wikipedia.org/wiki/Differential_evolution)
>
> I'd like it to be considered for inclusion in scipy.optimize, and have
> tried to write it as such. Can anyone give advice of how to go about
> polishing the code, such that it's suitable for inclusion in
> scipy.optimize?
>

Hi Andrew. What I'd like to see is some benchmarking to show that your
algorithm has at least comparable performance to optimize.basinhopping. DE
uses similar principles as simulated annealing (if with better performance
from what I can tell from a quick literature search), and we just
deprecated optimize.anneal because of its hopelessly poor performance. In
light in that experience I think that for any new optimization algorithm we
add we should first benchmark it.

Andrea Gavana has posted a nice set of benchmarks before:
http://article.gmane.org/gmane.comp.python.scientific.devel/18383, you
could contact him to add your algorithm (or do a similar comparison
yourself). Seeing your code in a comparison like
http://infinity77.net/global_optimization/multidimensional.html would be
useful.

Another question is if we think this is in scope for scipy.optimize, given
that PyGMO has this same algorithm and a number of similar ones.

Cheers,
Ralf



>
> https://github.com/andyfaff/DEsolver
>
> cheers,
> Andrew.
>
>
> --
> _____________________________________
> Dr. Andrew Nelson
>
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