GA4Stratification (version 1.0)

GA4StratificationP2: The genetic algorithm function to determine the stratum boundaries and sample sizes of each stratum in stratified sampling with Proportional Sample Allocation Scheme

Description

This is the general function in Genetic Algorithm that initially generates a random generation and then applies the fitness function, selects, mutates and crossovers in order to obtain the best solution.

Usage

GA4StratificationP2(dataName, numberOfStrata, sampleSize, iteration, GAgenerationSize, mutationRate)

Arguments

dataName

A string: The name of any type of numeric data containing the values of a univariate stratification variable.

numberOfStrata

An integer: The number of strata.

sampleSize

An integer: The total sample size.

iteration

An integer: The number of iterations in the Genetic Algorithm process.

GAgenerationSize

An integer: The number of the generations in the Genetic Algorithm process.

mutationRate

A numeric: The mutation rate in the Genetic Algorithm process. Mutation rate must be in between 0 and 1, inclusive. Small levels of mutation rate is preferable in Genetic Algorithm approach.

References

http://ideas.repec.org/a/eee/csdana/v52y2007i1p53-67.html

http://www.sciencedirect.com/science/article/B6V8V-4NHM520-1/2/a21e0295aa1616ff56da1ddf2c0ba1ac

See Also

GA4StratificationP2fit GA4StratificationP2fitt GA4StratificationP2m GA4StratificationP2x

GA4StratificationSelection randomnumGenerator

Examples

## The function is currently defined as
function(dataName,numberOfStrata,sampleSize,iteration,GAgenerationSize,mutationRate)
{
   dataName=data.frame(dataName)
   dataName=data.frame(dataName[order(dataName[,1]),])   
   lengthData=nrow(dataName)
   randomGeneration=array(0,dim=c(GAgenerationSize,lengthData))
   randomNumbers=array(0,dim=c(GAgenerationSize,numberOfStrata-1))
   cumTotal=cumsum(dataName)
   sumSquares=cumsum(dataName^2)
   
   nocrom=GAgenerationSize
   fitp1=array(0,dim=c(1,nocrom))
   fit=array(0,dim=c(nocrom,1))
   N=means=s=n=vars=mas=NN=k=p=t=array(0,dim=c(nocrom,numberOfStrata))

   dd=array(0,dim=c(nocrom,1))


	tableData=as.data.frame(table(dataName))
 	randomnumRange=cumsum(tableData[,2])
	lengthRandomnum=length(randomnumRange)


   for (i in 1:GAgenerationSize)
   {
	randomNumbers[i,]=randomnumGenerator(randomnumRange,lengthRandomnum,numberOfStrata-1)

   }
   

   son=array(lengthData,dim=c(GAgenerationSize,1))
   indis=array(c(1:GAgenerationSize,randomNumbers,son),dim=c(GAgenerationSize,(numberOfStrata+1)))

   for(i in 2:(numberOfStrata+1))
   {
   	randomGeneration[indis[,c(1,i)]]=1
   }

   bestValue=-99999999999999999999999999999999999999999999999999999999999999
   for ( i in 1:iteration )
   {

	fitnessValueGeneration=GA4StratificationP2fit(randomGeneration,dataName,numberOfStrata,sampleSize,cumTotal,sumSquares,lengthData,dd,nocrom,fitp1,fit,N,means,s,n,vars,mas,NN,k,p,t)
	if ( max(fitnessValueGeneration)>bestValue )
	{
	    bestValue=max(fitnessValueGeneration)
	    bestGeneration=randomGeneration[max(which(fitnessValueGeneration==bestValue)),]
	}

	randomGeneration=GA4StratificationSelection(randomGeneration,fitnessValueGeneration)

	randomGeneration=GA4StratificationP2x(randomGeneration,bestGeneration,dataName,numberOfStrata,sampleSize,fitnessValueGeneration,cumTotal,sumSquares,lengthData,dd,nocrom,fitp1,fit,N,means,s,n,vars,mas,NN,k,p,t)


	randomGeneration=GA4StratificationP2m(randomGeneration,mutationRate,i)

	fitnessValueGeneration=GA4StratificationP2fit(randomGeneration,dataName,numberOfStrata,sampleSize,cumTotal,sumSquares,lengthData,dd,nocrom,fitp1,fit,N,means,s,n,vars,mas,NN,k,p,t)

	if ( max(fitnessValueGeneration)>bestValue )
	{
	    bestValue=max(fitnessValueGeneration)
	    bestGeneration=randomGeneration[max(which(fitnessValueGeneration==bestValue)),]
	} else
	{
	randomGeneration[sample(GAgenerationSize,1),]=bestGeneration
	}

	cat(i, " ",-bestValue,'\n')
	flush.console()
	Sys.sleep(1)
   }
   GA4StratificationP2fitt(bestGeneration,dataName,numberOfStrata,sampleSize,-bestValue,cumTotal,sumSquares)
}