The DifferentialEvolution package

[Tags: library, mit]

Plain Differential Evolution algorithm for optimizing real-valued functions. For further info, see Differential evolution: a practical approach to global optimization By Kenneth V. Price, Rainer M. Storn, and Jouni A. Lampinen.

This Library is optimized and should achieve runtimes with factor of 2 from c. For optimal performance, pay some attention to rts memory parameters.

Example in GHCi:

import Data.Vector.Unboxed as VUB
import Numeric.Optimization.Algorithms.DifferentialEvolution

let fitness = VUB.sum . (*2)

de (defaultParams fitness ((VUB.replicate 60 0), (VUB.replicate 60 0)))


Versions0.0.1, 0.0.2
Change logNone available
Dependenciesbase (==4.*), deepseq (>=1.1 && <2), fclabels (==0.11.*), mtl (>2 && <=3), mwc-random (==0.8.*), parallel (>=3.1 && <4), primitive (>=0.3.1 && <4), vector (==0.7.*) [details]
AuthorVille Tirronen
CategoryNumerical, Optimization, Algorithms
Home page
UploadedFri Mar 11 11:59:14 UTC 2011 by VilleTirronen
Downloads513 total (13 in last 30 days)
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StatusDocs not available [build log]
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