R: R Based Genetic Algorithm (binary chromosome)

rbga.bin {genalg}

R Documentation

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=5, zeroToOneRatio=10,
         monitorFunc=NA, evalFunc=NA
         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 mutates. By default 1/(size+1).
`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.
`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.

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)