An R package for stochastic optimisation using Genetic Algorithms
The GA 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.
You can install the released version of GA from CRAN:
or the development version from GitHub:
# install.packages("devtools") devtools::install_github("luca-scr/GA")
Usage of the main functions and several examples are included in the papers shown in the references section below.
For an intro see the vignette A quick tour of GA, which is available as
Note that if the package is installed from GitHub the vignette is not automatically created. However, it can be created when installing from GitHub with the code:
devtools::install_github("luca-scr/GA", build_vignettes = TRUE)
The vignette is also available in the Get Started section on the GitHub web page of the package at http://luca-scr.github.io/GA/.
Scrucca, L. (2013) GA: A Package for Genetic Algorithms in R. Journal of Statistical Software, 53(4), 1-37. https://www.jstatsoft.org/article/view/v053i0
Scrucca, L. (2017) On some extensions to GA package: hybrid optimisation, parallelisation and islands evolution. The R Journal, 9(1), 187–206. https://journal.r-project.org/archive/2017/RJ-2017-008
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