GA: Genetic Algorithms

Flexible general-purpose toolbox implementing genetic algorithms (GAs) for stochastic optimisation. Binary, real-valued, and permutation representations are available to optimize a fitness function, i.e. a function provided by users depending on their objective function. Several genetic operators are available and can be combined to explore the best settings for the current task. Furthermore, users can define new genetic operators and easily evaluate their performances. Local search using general-purpose optimisation algorithms can be applied stochastically to exploit interesting regions. GAs can be run sequentially or in parallel, using an explicit master-slave parallelisation or a coarse-grain islands approach.

Version: 3.1.1
Depends: R (≥ 3.4), methods, foreach, iterators
Imports: stats, graphics, grDevices, utils, cli, crayon, Rcpp
LinkingTo: Rcpp, RcppArmadillo
Suggests: parallel, doParallel, doRNG (≥ 1.6), knitr (≥ 1.8)
Published: 2018-05-11
Author: Luca Scrucca ORCID iD [aut, cre]
Maintainer: Luca Scrucca <luca.scrucca at unipg.it>
BugReports: https://github.com/luca-scr/GA/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://luca-scr.github.io/GA/
NeedsCompilation: yes
Citation: GA citation info
Materials: NEWS
In views: Optimization
CRAN checks: GA results

Downloads:

Reference manual: GA.pdf
Vignettes: A quick tour of GA
Package source: GA_3.1.1.tar.gz
Windows binaries: r-devel: GA_3.1.1.zip, r-release: GA_3.1.1.zip, r-oldrel: GA_3.1.1.zip
OS X binaries: r-release: GA_3.1.1.tgz, r-oldrel: GA_3.0.2.tgz
Old sources: GA archive

Reverse dependencies:

Reverse depends: foreSIGHT, GAabbreviate, mcga, recmap, Rothermel, SPIGA
Reverse imports: autoSEM, datafsm, inlmisc, kernDeepStackNet, LCAvarsel, metacoder, nlr, TropFishR
Reverse suggests: MSCMT, PopED, regsem, seriation

Linking:

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