GA: Genetic Algorithms

An R package for optimisation using genetic algorithms. The package provides a flexible general-purpose set of tools for implementing genetic algorithms search in both the continuous and discrete case, whether constrained or not. Users can easily define their own objective function depending on the problem at hand. 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: 3.0.1
Depends: R (≥ 3.0), methods, foreach, iterators
Imports: stats, graphics, grDevices, utils
Suggests: parallel, doParallel, doRNG (≥ 1.6), knitr (≥ 1.8)
Published: 2016-05-16
Author: Luca Scrucca [aut, cre]
Maintainer: Luca Scrucca <luca.scrucca at unipg.it>
BugReports: NA
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: NA
NeedsCompilation: no
Citation: GA citation info
Materials: README NEWS
In views: Optimization
CRAN checks: GA results

Downloads:

Reference manual: GA.pdf
Vignettes: A quick tour of GA
Package source: GA_3.0.1.tar.gz
Windows binaries: r-devel: GA_3.0.1.zip, r-release: GA_3.0.1.zip, r-oldrel: GA_3.0.1.zip
OS X Mavericks binaries: r-release: GA_3.0.1.tgz, r-oldrel: GA_3.0.1.tgz
Old sources: GA archive

Reverse dependencies:

Reverse depends: GAabbreviate, mcga, Rothermel
Reverse imports: autoSEM, datafsm
Reverse suggests: PopED, regsem, seriation