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)))

Modules

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

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Versions [RSS] 0.0.1, 0.0.2
Dependencies base (>=4 && <5), deepseq (>=1.1 && <2), fclabels (>=0.11 && <0.12), mtl (>2 && <=3), mwc-random (>=0.8 && <0.9), parallel (>=3.1 && <4), primitive (>=0.3.1 && <4), vector (>=0.7 && <0.8) [details]
License MIT
Author Ville Tirronen
Maintainer ville.tirronen@jyu.fi
Category Numerical, Optimization, Algorithms
Home page http://yousource.it.jyu.fi/optimization-with-haskell
Uploaded by VilleTirronen at 2011-03-11T11:59:14Z
Distributions NixOS:0.0.2
Downloads 2158 total (9 in the last 30 days)
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Status Docs not available [build log]
All reported builds failed as of 2016-10-25 [all 15 reports]