Package 'genalg'

R Based Genetic Algorithm Plot Function

Description

Plots features of the genetic algorithm optimization run. The default plot shows the minimal and mean evaluation value, indicating how far the GA has progressed.

The "hist" plot shows for binary chromosome the gene selection frequency, i.e. the times one gene in the chromosome was selected in the current population. In case of floats chromosomes, it will make histograms for each variable to indicate the selected values in the population.

The "vars" plot the evaluation function versus the variable value. This is useful to look at correlations between the variable and the evaluation values.

Usage

## S3 method for class 'rbga'
plot(x, type="default", breaks=10, ...)

Arguments

x	a rbga object.
type	one of "hist", "vars" or "default".
breaks	the number of breaks in a histogram.
...	options directly passed to the plot function.

Examples

evaluate <- function(string=c()) {
    returnVal = 1 / sum(string);
    returnVal
}

rbga.results = rbga.bin(size=10, mutationChance=0.01, zeroToOneRatio=0.5,
    evalFunc=evaluate)

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

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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.

See Also

rbga.bin plot.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")

---

R Based Genetic Algorithm (binary chromosome)

Description

A R based genetic algorithm that optimizes, using a user set evaluation function, a binary chromosome which can be used for variable selection. 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 binary chromosome, a vector of zeros and ones. 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.bin(size=10,
         suggestions=NULL,
         popSize=200, iters=100, 
         mutationChance=NA,
         elitism=NA, zeroToOneRatio=10,
         monitorFunc=NULL, evalFunc=NULL,
         showSettings=FALSE, verbose=FALSE)

Arguments

size	the number of genes in the chromosome.
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.
zeroToOneRatio	the change for a zero for mutations and initialization. This option is used to control the number of set genes in the chromosome. For example, when doing variable selectionm this parameter should be set high to
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.
suggestions	optional list of suggested chromosomes

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.

See Also

rbga plot.rbga

Examples

# a very simplistic optimization
evaluate <- function(string=c()) {
    returnVal = 1 / sum(string);
    returnVal
}

rbga.results = rbga.bin(size=10, mutationChance=0.01, zeroToOneRatio=0.5,
    evalFunc=evaluate)

plot(rbga.results)

# in this example the four variables in the IRIS data 
# set are complemented with 36 random variables. 
# Variable selection should find the four original
# variables back (example by Ron Wehrens).
## Not run: 
data(iris)
library(MASS)
X <- cbind(scale(iris[,1:4]), matrix(rnorm(36*150), 150, 36))
Y <- iris[,5]

iris.evaluate <- function(indices) {
  result = 1
  if (sum(indices) > 2) {
    huhn <- lda(X[,indices==1], Y, CV=TRUE)$posterior
    result = sum(Y != dimnames(huhn)[[2]][apply(huhn, 1,
               function(x)
               which(x == max(x)))]) / length(Y)
  }
  result
}

monitor <- function(obj) {
    minEval = min(obj$evaluations);
    plot(obj, type="hist");
}

woppa <- rbga.bin(size=40, mutationChance=0.05, zeroToOneRatio=10,
  evalFunc=iris.evaluate, verbose=TRUE, monitorFunc=monitor)

## End(Not run)

# another realistic example: wavelenght selection for PLS on NIR data
## Not run: 
library(pls.pcr)
data(NIR)

numberOfWavelenghts = ncol(NIR$Xtrain)
evaluateNIR <- function(chromosome=c()) {
    returnVal = 100
    minLV = 2
    if (sum(chromosome) < minLV) {
        returnVal
    } else {
        xtrain = NIR$Xtrain[,chromosome == 1];
        pls.model = pls(xtrain, NIR$Ytrain, validation="CV", grpsize=1, 
                        ncomp=2:min(10,sum(chromosome)))
        returnVal = pls.model$val$RMS[pls.model$val$nLV-(minLV-1)]
        returnVal
    }
}

monitor <- function(obj) {
    minEval = min(obj$evaluations);
    filter = obj$evaluations == minEval;
    bestObjectCount = sum(rep(1, obj$popSize)[filter]);
    # ok, deal with the situation that more than one object is best
    if (bestObjectCount > 1) {
        bestSolution = obj$population[filter,][1,];
    } else {
        bestSolution = obj$population[filter,];
    }
    outputBest = paste(obj$iter, " #selected=", sum(bestSolution),
                       " Best (Error=", minEval, "): ", sep="");
    for (var in 1:length(bestSolution)) {
        outputBest = paste(outputBest,
            bestSolution[var], " ",
            sep="");
    }
    outputBest = paste(outputBest, "\n", sep="");

    cat(outputBest);
}

nir.results = rbga.bin(size=numberOfWavelenghts, zeroToOneRatio=10, 
    evalFunc=evaluateNIR, monitorFunc=monitor,
    popSize=200, iters=100, verbose=TRUE)

## End(Not run)

---

R Based Genetic Algorithm Summary Function

Description

Summarizes the genetic algorithm results.

Usage

## S3 method for class 'rbga'
summary(object, echo=FALSE, ...)

Arguments

object	a rbga object.
echo	if true, the summary will be printed to STDOUT as well as returned.
...	other options (ignored)

Examples

evaluate <- function(string=c()) {
    returnVal = 1 / sum(string);
    returnVal
}

rbga.results = rbga.bin(size=10, mutationChance=0.01, zeroToOneRatio=0.5,
    evalFunc=evaluate)

summary(rbga.results)

---