DifferentialEvolution: Global optimization using Differential Evolution

[ algorithms, library, mit, numerical, optimization ] [ Propose Tags ]

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 . VUB.map (*2)

de (defaultParams fitness ((VUB.replicate 60 0), (VUB.replicate 60 0)))
Versions 0.0.1, 0.0.2
Dependencies base (==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]
License MIT
Author Ville Tirronen
Maintainer ville.tirronen@jyu.fi
Category Numerical, Optimization, Algorithms
Uploaded by VilleTirronen at Fri Mar 11 11:00:53 UTC 2011
Distributions NixOS:0.0.2
Downloads 1132 total (9 in the last 30 days)
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Status Docs not available [build log]
All reported builds failed as of 2016-12-28 [all 7 reports]
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Modules

  • Numeric
    • Optimization
      • Algorithms
        • Numeric.Optimization.Algorithms.DifferentialEvolution

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