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Asynchronous Differential Evolution, with efficient multiprocessing.

Project description

Performs the Differential Evolution (DE) algorithm asynchronously. With a multiprocess evaluation function running on a multicore CPU or cluster, ade can get the DE processing done several times faster than standard single-threaded DE. It does this without departing in any way from the numeric operations performed by the classic Storn and Price algorithm with either a randomly chosen candidate or the best available candidate.

You get a substantial multiprocessing speed-up and the well-understood, time-tested behavior of the classic DE/rand/1/bin or DE/best/1/bin algorithm. (You can pick which one to use.) The underlying numeric recipe is not altered at all, but everything runs a lot faster.

The ade package also does simple and smart population initialization, informative progress reporting, adaptation of the vector differential scaling factor F based on how much each generation is improving, and automatic termination after a reasonable level of convergence to the best solution.

For a tutorial and usage examples, see the project page at

Project details

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Filename, size & hash SHA256 hash help File type Python version Upload date
ade-0.8.3.tar.gz (46.6 kB) Copy SHA256 hash SHA256 Source None Jul 14, 2018

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