# rbga

0th

Percentile

##### R Based Genetic Algorithm (floating point chromosome)

A R based genetic algorithm that optimizes, using a user set evaluation function, a set of floats. It takes as input minimum and maximum values for the floats to optimizes. The optimum is the chromosome for which the evaluation value is minimal. It requires a evalFunc method to be supplied that takes as argument the chromosome, a vector of floats. Additionally, the GA optimization can be monitored by setting a monitorFunc that takes a rbga object as argument. Results can be visualized with plot.rbga and summarized with summary.rbga.

Keywords
multivariate
##### Usage
rbga(stringMin=c(), stringMax=c(), suggestions=NULL, popSize=200, iters=100, mutationChance=NA, elitism=NA, monitorFunc=NULL, evalFunc=NULL, showSettings=FALSE, verbose=FALSE)
##### Arguments
stringMin
vector with minimum values for each gene.
stringMax
vector with maximum values for each gene.
suggestions
optional list of suggested chromosomes
popSize
the population size.
iters
the number of iterations.
mutationChance
the chance that a gene in the chromosome mutates. By default 1/(size+1). It affects the convergence rate and the probing of search space: a low chance results in quicker convergence, while a high chance increases the span of the search space.
elitism
the number of chromosomes that are kept into the next generation. By default is about 20% of the population size.
monitorFunc
Method run after each generation to allow monitoring of the optimization
evalFunc
User supplied method to calculate the evaluation function for the given chromosome
showSettings
if true the settings will be printed to screen. By default False.
verbose
if true the algorithm will be more verbose. By default False.
##### References

C.B. Lucasius and G. Kateman (1993). Understanding and using genetic algorithms - Part 1. Concepts, properties and context. Chemometrics and Intelligent Laboratory Systems 19:1-33. C.B. Lucasius and G. Kateman (1994). Understanding and using genetic algorithms - Part 2. Representation, configuration and hybridization. Chemometrics and Intelligent Laboratory Systems 25:99-145.

rbga.bin plot.rbga

• rbga
##### Examples
# optimize two values to match pi and sqrt(50)
evaluate <- function(string=c()) {
returnVal = NA;
if (length(string) == 2) {
returnVal = abs(string[1]-pi) + abs(string[2]-sqrt(50));
} else {
stop("Expecting a chromosome of length 2!");
}
returnVal
}

monitor <- function(obj) {
# plot the population
xlim = c(obj$stringMin[1], obj$stringMax[1]);
ylim = c(obj$stringMin[2], obj$stringMax[2]);
plot(obj\$population, xlim=xlim, ylim=ylim,
xlab="pi", ylab="sqrt(50)");
}

rbga.results = rbga(c(1, 1), c(5, 10), monitorFunc=monitor,
evalFunc=evaluate, verbose=TRUE, mutationChance=0.01)

plot(rbga.results)
plot(rbga.results, type="hist")
plot(rbga.results, type="vars")

Documentation reproduced from package genalg, version 0.2.0, License: GPL-2

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