rbga.bin: R Based Genetic Algorithm (binary chromosome)

In genalg: R Based Genetic Algorithm

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)

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