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

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")

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

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

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

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)