[SciPy-Dev] Robert Kern's accursed excellent DE implementation

[Andrew Nelson]

Scipy 0.15 now has a (serial) DE implementation. I'd be interested to now
how the two implementations compare in terms of total number of function
evaluations. There are a few papers out there that discuss parallel DE, but
I can't remember where they were. I'd be interested to know how easy it is
to parallelise function evaluations in scipy. I'm guessing cython and
openmp might be a way to go. It's a pity that clang doesn't have openmp.
On 25/03/2015 6:00 AM, "James Phillips" <zunzun at zunzun.com> wrote:

> I have been using Robert Kern's implementation of the Differential
> Evolution (DE) genetic algorithm for a decade, for the purpose of
> guessimating initial parameter estimates for curve fitting and surface
> fitting in my oprn source pyeq2 fitting library.
>
> I can't find anything that works better, which gives rise to my current
> problem.
>
> In trying to improve performance of my fitting library, I tried to use GPU
> calculations for each generation of the genetic algorithm, I found the
> following:
>
> 1) Robert's 2005 implementation of DE is not parallelizeable, as each
> crossover withing a generation can affect the population from which new
> items will be created.  That is, within a given generation the population
> *changes* as the algorithm runs the generation itself, and it must run
> serially in it's present form.
>
> 2) I can rework the algorithm to be parallelizeable by separating out
> "crossover", "breeding" and "evolving" into three separate steps, but
> months of testing show that population size and number of generations must
> beconsiderably  increased to match the results from Robert's version.  That
> is, making the algorithm parallelizable means slowing it down so I can
> speed it up!
>
> I would like to increase performance, but cannot find any way to equal
> Robert's results without reducing performance prior to parallelization.
>
> Any suggestions?
>
> James
>
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