// Select individuals for crossover
private static Individual rouletteSelection(Population pop)
{
double[] weights = new double[pop.size()];
for (int i = 0; i < pop.size(); i++)
{
weights[i] = FitnessCalc.getProb(pop.getIndividual(i));
}
double weight_sum = 0;
for (int i = 0; i < weights.Length; i++)
{
weight_sum += weights[i];
}
// get a random value
double value = randUniformPositive() * weight_sum;
// locate the random value based on the weights
for (int i = 0; i < weights.Length; i++)
{
value -= weights[i];
if (value <= 0)
{
return(pop.getIndividual(i));
}
}
// only when rounding errors occur
return(pop.getIndividual(weights.Length - 1));
}
public static Individual getFittest()
{
Individual fittest = currentPopulation.getIndividual(0);
for (int i = 0; i < currentPopulation.size(); i++)
{
if (betterFitness(fittest, i))
{
fittest = currentPopulation.getIndividual(i);
}
}
return(fittest);
}
//Evolve population
public static Population evolvePopulation(Population pop)
{
Population newPopulation = new Population(pop.size(), false);
Individual fittestOfThisPop = null;
//keep your best individual
if (Elitism)
{
//get fittest of the CURRENT pop
fittestOfThisPop = newPopulation.saveIndividual(0, FitnessCalc.getFittest());
//SAVE best of the algorithm
if (bestSolution != null)
{
if (betterFitness(bestSolution, fittestOfThisPop))
{
bestSolution = fittestOfThisPop;
}
}
else
{
bestSolution = fittestOfThisPop;
}
}
//Crossover population
int elitismOffset;
if (Elitism)
{
elitismOffset = 1;
}
else
{
elitismOffset = 0;
}
//loop over tje population size and create new individuals with crossover
for (int i = elitismOffset; i < pop.size(); i++)
{
Individual indiv1 = rouletteSelection(pop);
Individual indiv2 = rouletteSelection(pop);
Individual newIndiv = crossover(indiv1, indiv2);
newPopulation.saveIndividual(i, newIndiv);
}
//Mutate population
for (int i = elitismOffset; i < newPopulation.size(); i++)
{
mutate(newPopulation.getIndividual(i));
}
// fitless die & replace by fittest
if (DieFactor)
{
if (elitism)
{
newPopulation.saveIndividual(FitnessCalc.getFitlessIndex(), fittestOfThisPop);
}
else
{
newPopulation.saveIndividual(FitnessCalc.getFitlessIndex(), FitnessCalc.getFittest());
}
}
return(newPopulation);
}
// Select individuals for crossover
private static Individual rouletteSelection(Population pop)
{
double[] weights = new double[pop.size()];
for(int i =0; i < pop.size(); i++) {
weights[i] = FitnessCalc.getProb(pop.getIndividual(i));
}
double weight_sum = 0;
for (int i = 0; i < weights.Length; i++) {
weight_sum += weights[i];
}
// get a random value
double value = randUniformPositive() * weight_sum;
// locate the random value based on the weights
for (int i = 0; i < weights.Length; i++) {
value -= weights[i];
if (value <= 0) return pop.getIndividual(i);
}
// only when rounding errors occur
return pop.getIndividual(weights.Length - 1);
}
//Evolve population
public static Population evolvePopulation(Population pop)
{
Population newPopulation = new Population(pop.size(), false);
Individual fittestOfThisPop = null;
//keep your best individual
if (Elitism) {
//get fittest of the CURRENT pop
fittestOfThisPop = newPopulation.saveIndividual(0, FitnessCalc.getFittest());
//SAVE best of the algorithm
if(bestSolution != null) {
if (betterFitness(bestSolution, fittestOfThisPop)) {
bestSolution = fittestOfThisPop;
}
} else {
bestSolution = fittestOfThisPop;
}
}
//Crossover population
int elitismOffset;
if (Elitism) {
elitismOffset = 1;
} else {
elitismOffset = 0;
}
//loop over tje population size and create new individuals with crossover
for(int i = elitismOffset; i<pop.size();i++) {
Individual indiv1 = rouletteSelection(pop);
Individual indiv2 = rouletteSelection(pop);
Individual newIndiv = crossover(indiv1, indiv2);
newPopulation.saveIndividual(i, newIndiv);
}
//Mutate population
for (int i=elitismOffset; i<newPopulation.size();i++) {
mutate(newPopulation.getIndividual(i));
}
// fitless die & replace by fittest
if (DieFactor) {
if (elitism) {
newPopulation.saveIndividual(FitnessCalc.getFitlessIndex(), fittestOfThisPop);
} else {
newPopulation.saveIndividual(FitnessCalc.getFitlessIndex(), FitnessCalc.getFittest());
}
}
return newPopulation;
}