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
Home page	http://yousource.it.jyu.fi/optimization-with-haskell
Uploaded	by VilleTirronen at Fri Mar 11 11:59:14 UTC 2011
Distributions	NixOS:0.0.2
Downloads	1132 total (11 in the last 30 days)
Rating	(no votes yet) [estimated by rule of succession]
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Status	Docs not available [build log] All reported builds failed as of 2016-10-25 [all 15 reports] Hackage Matrix CI

Modules

Numeric
- Optimization
  - Algorithms
    - Numeric.Optimization.Algorithms.DifferentialEvolution

Downloads

DifferentialEvolution-0.0.2.tar.gz [browse] (Cabal source package)
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