/// <summary>
/// Generates a random set of genes for this chromosome
/// </summary>
public void GenerateRandomGenes()
{
for (int i = 0; i < genes.Length; i++)
{
genes[i] = Friends.GetRandomDouble();
}
}
/// <summary>
/// Single point crossover
/// </summary>
/// <param name="probability"></param>
public void CrossoverPop(double probability)
{
if (probability > 0)
{
int size = chromosomes[0].GetGenes().Length;
for (int i = 0; i < chromosomes.Length; i += 2)
{
if (Friends.GetRandomDouble() < probability)
{
int splice = Friends.GetRandomInt(0, size);
double[] limbo1 = new double[size];
double[] limbo2 = new double[size];
chromosomes[i].GetGenes().CopyTo(limbo1, 0);
chromosomes[i + 1].GetGenes().CopyTo(limbo2, 0);
for (int s = splice; s < size; s++)
{
limbo1[s] = chromosomes[i + 1].GetGenes()[s];
limbo2[s] = chromosomes[i].GetGenes()[s];
}
chromosomes[i].SetGenes(limbo1);
chromosomes[i + 1].SetGenes(limbo2);
}
}
}
}
/// <summary>
/// Replaces the population with a new one based on fitness using Roulette Wheel selection
/// </summary>
public void SelectPop()
{
// First Elitism, then Roulette Wheel technique as described by Melanie Mitchell, An Introduction to GAs, p.166
double fitSum;
double totalFitness = 0.0;
int counter = 0;
// Set up a fresh new population (no clone).
Population newPop = new Population(this.chromosomes.Length, popSliders, popGenePools, owner, -1);
// Copy the elites
for (int i = 0; i < chromosomes.Length; i++)
{
if (chromosomes[i].GetFitness() == 1.0)
{
newPop.chromosomes[counter] = new Chromosome(chromosomes[i]);
newPop.chromosomes[counter].isRepresentative = false;
newPop.chromosomes[counter].isElite = true;
counter++;
}
}
// Now for the roulette wheel selection for the new population
for (int i = 0; i < chromosomes.Length; i++)
{
totalFitness += chromosomes[i].GetFitness();
}
// Run from the counter above
for (int i = counter; i < newPop.chromosomes.Length; i++)
{
double weightedRandom = Friends.GetRandomDouble() * totalFitness;
fitSum = 0.0;
for (int j = 0; j < chromosomes.Length; j++)
{
fitSum += chromosomes[j].GetFitness();
// Stop when we get to the right design
// higher fitnesses will result in a greater jump in fitsum, and therefore more likely to be above the random number.
if (fitSum > weightedRandom)
{
// Clone the chromosomes, reset all the representatives
// Fitness is cloned at this stage
newPop.chromosomes[i] = new Chromosome(chromosomes[j]);
newPop.chromosomes[i].isRepresentative = false;
newPop.chromosomes[i].isElite = false;
break;
}
}
}
// Finally, Replace the current popultion of chromosomes with the newPop
for (int i = 0; i < chromosomes.Length; i++)
{
chromosomes[i] = new Chromosome(newPop.chromosomes[i]);
}
}
/// <summary>
/// Goes through each gene and mutates it depending on a given probability
/// </summary>
/// <param name="probability"> probability that a single gene in the chomrosome will mutate</param>
public void Mutate(double probability)
{
for (int i = 0; i < genes.Length; i++)
{
double tempRand = Friends.GetRandomDouble();
if (tempRand < probability)
{
genes[i] = Friends.GetRandomDouble();
}
}
}
/// <summary>
/// Shakes the genes a little
/// </summary>
/// <param name="t">amount to jiggle the genes by from 0.0 to 1.0</param>
public void JiggleGenes(double t)
{
for (int i = 0; i < genes.Length; i++)
{
// Note, you can't risk having two chromosomes with the same genes, even 0.0 and 1.0
genes[i] += (0.5 * t - Friends.GetRandomDouble() * t);
if (genes[i] < 0.0)
{
genes[i] = 0.0 + Friends.GetRandomDouble() * 0.001;
}
if (genes[i] > 1.0)
{
genes[i] = 1.0 - Friends.GetRandomDouble() * 0.001;
}
}
}
/// <summary>
/// Replaces the population with a new one based on fitness using Roulette Wheel selection
/// </summary>
public void RoulettePop()
{
// Roulette Wheel technique here as described by Melanie Mitchell, An Introduction to GAs, p.166
double fitSum;
double totalFitness = 0.0;
// Set up a fresh population.
Population newPop = new Population(this.chromosomes.Length, popSliders, popGenePools, owner, -1);
// find the total fitness
for (int i = 0; i < chromosomes.Length; i++)
{
totalFitness += chromosomes[i].GetFitness();
}
// Now for the roulette wheel selection for the new population
for (int i = 0; i < newPop.chromosomes.Length; i++)
{
double weightedRandom = Friends.GetRandomDouble() * totalFitness;
fitSum = 0.0;
for (int j = 0; j < chromosomes.Length; j++)
{
fitSum += chromosomes[j].GetFitness();
if (fitSum > weightedRandom)
{
// Clone the chromosomes, but make sure to reset if the design is a cluster representative
newPop.chromosomes[i] = chromosomes[j].Clone();
newPop.chromosomes[i].isRepresentative = false;
break;
}
}
}
// Replace the current popultion of chromosomes with the newPop
for (int i = 0; i < chromosomes.Length; i++)
{
chromosomes[i] = newPop.chromosomes[i].Clone();
}
// Make sure to reset all the fitnesses here (selection has now already occured).
ResetAllFitness();
}
