GA v3.1.1

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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.

Functions in GA

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

Name
GA.Rmd
vignette.css
vignette2.css
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Details

Date 2018-05-11
LinkingTo Rcpp, RcppArmadillo
License GPL (>= 2)
VignetteBuilder knitr
URL https://luca-scr.github.io/GA/
BugReports https://github.com/luca-scr/GA/issues
Repository CRAN
ByteCompile true
LazyLoad yes
NeedsCompilation yes
Packaged 2018-05-11 09:04:46 UTC; luca
Date/Publication 2018-05-11 13:13:07 UTC

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