rbga.bin.logistic = function(size=10, suggestions=NULL, popSize=200, iters=100, mutationChance=NA, elitism=NA, zeroToOneRatio=10, monitorFunc=NULL, evalFunc=NULL, showSettings=FALSE, verbose=FALSE, data, output, family, weights) {
if (is.null(evalFunc)) {
stop("A evaluation function must be provided. See the evalFunc parameter.")
}
vars = size
if (is.na(mutationChance)) {
mutationChance = 1/(vars + 1)
}
if (is.na(elitism)) {
elitism = floor(popSize/5)
}
if (verbose)
cat("Testing the sanity of parameters...\n")
if (popSize < 5) {
stop("The population size must be at least 5.")
}
if (iters < 1) {
stop("The number of iterations must be at least 1.")
}
if (!(elitism < popSize)) {
stop("The population size must be greater than the elitism.")
}
if (showSettings) {
if (verbose) cat("The start conditions:\n")
result = list(size=size, suggestions=suggestions,
popSize=popSize, iters=iters, elitism=elitism,
mutationChance=mutationChance)
class(result) = "rbga"
cat(summary(result))
}
else {
if (verbose)
cat("Not showing GA settings...\n")
}
if (vars > 0) {
if (!is.null(suggestions)) {
if (verbose) cat("Adding suggestions to first population...\n")
population = matrix(nrow=popSize, ncol=vars)
suggestionCount = dim(suggestions)[1]
for (i in 1:suggestionCount) {
population[i,] = suggestions[i,]
}
if (verbose) cat("Filling others with random values in the given domains...\n")
for (child in (suggestionCount + 1):popSize) {
population[child, ] = sample(c(rep(0, zeroToOneRatio), 1), vars, rep=TRUE)
while (sum(population[child, ]) == 0) {
population[child, ] = sample(c(rep(0, zeroToOneRatio), 1), vars, rep=TRUE)
}
}
}
else {
if (verbose)
cat("Starting with random values in the given domains...\n")
population = matrix(nrow=popSize, ncol=vars)
for (child in 1:popSize) {
population[child,] = sample(c(rep(0, zeroToOneRatio), 1), vars, rep=TRUE)
while (sum(population[child,]) == 0) {
population[child,] = sample(c(rep(0, zeroToOneRatio), 1), vars, rep=TRUE)
}
}
}
bestEvals = rep(NA, iters)
meanEvals = rep(NA, iters)
evalVals = rep(NA, popSize)
for (iter in 1:iters) {
if (verbose)
cat(paste("Starting iteration", iter, "\n"))
if (verbose)
cat("Calucating evaluation values... ")
for (object in 1:popSize) {
if (is.na(evalVals[object])) {
evalVals[object] = evalFunc(population[object,], data=data, output=output, family=family, weights=weights)
if (verbose)
cat(".")
}
}
bestEvals[iter] = min(evalVals)
meanEvals[iter] = mean(evalVals)
if (verbose)
cat(" done.\n")
if (!is.null(monitorFunc)) {
if (verbose)
cat("Sending current state to rgba.monitor()...\n")
result = list(type="binary chromosome", size=size,
popSize=popSize, iter=iter, iters=iters,
population=population, elitism=elitism,
mutationChance=mutationChance, evaluations=evalVals,
best=bestEvals, mean=meanEvals)
class(result) = "rbga"
monitorFunc(result, output=output, data=data)
}
if (iter < iters) {
if (verbose)
cat("Creating next generation...\n")
newPopulation = matrix(nrow = popSize, ncol = vars)
newEvalVals = rep(NA, popSize)
if (verbose)
cat(" sorting results...\n")
sortedEvaluations = sort(evalVals, index = TRUE)
sortedPopulation = matrix(population[sortedEvaluations$ix,], ncol=vars)
if (elitism > 0) {
if (verbose) cat(" applying elitism...\n")
newPopulation[1:elitism,] = sortedPopulation[1:elitism,]
newEvalVals[1:elitism] = sortedEvaluations$x[1:elitism]
}
if (vars > 1) {
if (verbose)
cat(" applying crossover...\n")
for (child in (elitism + 1):popSize) {
parentProb = dnorm(1:popSize, mean=0, sd=(popSize/3))
parentIDs = sample(1:popSize, 2, prob=parentProb)
parents = sortedPopulation[parentIDs,]
crossOverPoint = sample(0:vars, 1)
if (crossOverPoint == 0) {
newPopulation[child,] = parents[2,]
newEvalVals[child] = sortedEvaluations$x[parentIDs[2]]
}
else if (crossOverPoint == vars) {
newPopulation[child,] = parents[1,]
newEvalVals[child] = sortedEvaluations$x[parentIDs[1]]
}
else {
newPopulation[child,] = c(parents[1,][1:crossOverPoint], parents[2,][(crossOverPoint+1):vars])
while (sum(newPopulation[child, ]) == 0) {
newPopulation[child,]=sample(c(rep(0, zeroToOneRatio), 1), vars, rep=TRUE)
}
}
}
}
else {
if (verbose) cat(" cannot crossover (#vars=1), using new randoms...\n")
newPopulation[(elitism+1):popSize,] = sortedPopulation[sample(1:popSize, popSize-elitism),]
}
population = newPopulation
evalVals = newEvalVals
if (mutationChance > 0) {
if (verbose) cat(" applying mutations... ")
mutationCount = 0
for (object in (elitism + 1):popSize) {
for (var in 1:vars) {
if (runif(1) < mutationChance) {
population[object, var] = sample(c(rep(0, zeroToOneRatio), 1), 1)
mutationCount = mutationCount + 1
}
}
}
if (verbose) cat(paste(mutationCount, "mutations applied\n"))
}
}
}
}
result = list(type="binary chromosome", size=size, popSize=popSize,
iters=iters, suggestions=suggestions, population=population,
elitism=elitism, mutationChance=mutationChance, evaluations=evalVals,
best=bestEvals, mean=meanEvals)
class(result) = "rbga"
return(result)
}
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