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GA  

Genetic Algorithms
View on CRAN: Click here


Download and install GA package within the R console
Install from CRAN:
install.packages("GA")

Install from Github:
library("remotes")
install_github("cran/GA")

Install by package version:
library("remotes")
install_version("GA", "3.2.5")



Attach the package and use:
library("GA")
Maintained by
Luca Scrucca
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2012-06-22
Latest Update: 2024-01-28
Description:
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. For more details see Scrucca (2013) and Scrucca (2017) .
How to cite:
Luca Scrucca (2012). GA: Genetic Algorithms. R package version 3.2.5, https://cran.r-project.org/web/packages/GA. Accessed 09 Oct. 2026.
Previous versions and publish date:
1.0 (2012-06-22 12:37), 1.1 (2013-04-08 13:48), 2.0 (2013-08-20 15:23), 2.1 (2014-05-06 12:30), 2.2 (2014-10-15 13:20), 3.0.1 (2016-05-16 14:40), 3.0.2 (2016-06-07 13:54), 3.0 (2016-05-10 23:35), 3.1.1 (2018-05-11 15:13), 3.1 (2018-05-09 11:56), 3.2.1 (2021-04-21 08:00), 3.2.2 (2021-10-15 12:40), 3.2.3 (2022-10-19 01:38), 3.2.4 (2024-01-28 12:50), 3.2 (2019-01-10 13:00), (2026-07-09 08:04)
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View GA citation profile
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Complete documentation for GA
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