GA v3.0.2

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by Luca Scrucca

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.

Readme

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GA

An R package for optimization 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.

Installation

Get the released version from CRAN:

install.packages("GA")

Or the development version from github:

# install.packages("devtools")
devtools::install_github("luca/GA")

How to Use This Package

See the papers in the references section below. A quick intro vignette is also available, which can be accessed using

vignette("GA")

References:

Scrucca, L. (2013) GA: A Package for Genetic Algorithms in R. Journal of Statistical Software, 53(4), 1-37. URL http://www.jstatsoft.org/v53/i04/.

Scrucca, L. (2016) On some extensions to GA package: hybrid optimisation, parallelisation and islands evolution. Submitted to R Journal. Pre-print available at http://arxiv.org/abs/1605.01931.

Functions in GA

Name Description
GA-package Genetic Algorithms
binary2decimal Binary encoding of decimal numbers and viceversa.
binary2gray Gray encoding for binary strings
persp3D Perspective plot with colour levels
plot.ga-method Plot of Genetic Algorithm search path
gaisl-class Class "gaisl"
gaisl Islands Genetic Algorithms
gaControl A function for setting or retrieving defaults genetic operators
ga Genetic Algorithms
plot.gaisl-method Plot of Islands Genetic Algorithm search path
summary.ga-method Summary for Genetic Algorithms
palettes Colors palettes
parNames-methods Parameters or decision variables names from an object of class ga-class.
summary.gaisl-method Summary for Islands Genetic Algorithms
gaSummary Summarize genetic algorithm evolution
numericOrNA-class Virtual Class "numericOrNA" - Simple Class for subassignment Values
ga-class Class "ga"
ga_Mutation Mutation operators in genetic algorithms
ga_Crossover Crossover operators in genetic algorithms
ga_Selection Selection operators in genetic algorithms
ga_Population Population initialization in genetic algorithms
ga_pmutation Variable mutation probability in genetic algorithms
GA-internal Internal GA functions
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Details

Date 2016-06-07
License GPL (>= 2)
ByteCompile true
LazyLoad yes
VignetteBuilder knitr
URL https://github.com/luca-scr/GA
Repository CRAN
NeedsCompilation no
Packaged 2016-06-07 07:42:26 UTC; luca
Date/Publication 2016-06-07 13:54:06

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