rbga: R Based Genetic Algorithm (floating point chromosome)
In genalg: R Based Genetic Algorithm

rbga	R Documentation

R Based Genetic Algorithm (floating point chromosome)

Description

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.

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.

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")

genalg documentation built on April 5, 2022, 1:10 a.m.