public float FitnessFunction(int index)
{
float score = 0;
GenerateQP <Gene> dna = ga.Population[index];
for (int i = 0; i < CLOCourse.Count; i++)
{
for (int j = 0; j < dna.Genes.Length; j++)
{
//foreach (string i in QuestionPool.Gene)
//{
// for (int j = 0; j <dna.Genes.Length; j++)
//{
if (CLOCourse[i].Equals(dna.Genes[j].CLO))
{
score++;
}
break;
//}
}
//score /= CLOSum.Count;
}
return(score);
}
public void NewGeneration(int numNewDNA = 0, bool crossoverNewDNA = false)
{
int finalCount = Population.Count + numNewDNA;
if (finalCount <= 0)
{
return;
}
if (Population.Count > 0)
{
CalculateFitness();
Population.Sort(CompareDNA);
}
newPopulation.Clear();
for (int i = 0; i < Population.Count; i++)
{
if (i < Elitism && i < Population.Count)
{
newPopulation.Add(Population[i]);
}
else if (i < Population.Count || crossoverNewDNA)
{
GenerateQP <T> parent1 = ChooseParent();
GenerateQP <T> parent2 = ChooseParent();
//if (parent1 == null)
//{
// return;
//}
if (parent1 == null)
{
HttpContext.Current.Response.Write("NULL");
}
GenerateQP <T> child = parent1.Crossover(parent2);
// if (child == null)
//{
// return;
//}
child.Mutate(MutationRate);
newPopulation.Add(child);
}
else
{
newPopulation.Add(new GenerateQP <T>(dnaSize, random, getRandomGene, fitnessFunction, shouldInitGenes: true));
}
}
List <GenerateQP <T> > tmpList = Population;
Population = newPopulation;
newPopulation = tmpList;
Generation++;
}
public GenerateQP <T> Crossover(GenerateQP <T> otherParent)
{
GenerateQP <T> child = new GenerateQP <T>(Genes.Length, random, getRandomGene, fitnessFunction, shouldInitGenes: false);
for (int i = 0; i < Genes.Length; i++)
{
child.Genes[i] = random.NextDouble() < 0.5 ? Genes[i] : otherParent.Genes[i];
}
return(child);
}
public void NewGeneration(int numNewDNA = 0, bool crossoverNewDNA = false)
{
int finalCount = popsize + numNewDNA;
if (finalCount <= 0)
{
return;
}
if (popsize > 0)
{
CalculateFitness();
Population.Sort(CompareDNA);
}
newPopulation.Clear();
for (int i = 0; i < popsize; i++)
{
if (i < Elitism && i < popsize)
{
newPopulation.Add(Population[i]);
}
else if (crossoverNewDNA)
{
GenerateQP <T> parent1 = ChooseParent();
GenerateQP <T> parent2 = ChooseParent();
//if (parent1 == null)
//{
// return;
//}
GenerateQP <T> child = parent1.Crossover(parent2);
// if (child == null)
//{
// return;
//}
//child.Mutate(MutationRate);
newPopulation.Add(child);
}
else
{
GenerateQP <T> generateclone = new GenerateQP <T>(dnaSize, random, getRandomGene, fitnessFunction, shouldInitGenes: true);
newPopulation.Add((GenerateQP <T>)generateclone.Clone());
}
}
List <GenerateQP <T> > tmpList = new List <GenerateQP <T> >(Population);
Population = new List <GenerateQP <T> >(newPopulation);
newPopulation = new List <GenerateQP <T> >(tmpList);
Generation++;
}
private int CompareDNA(GenerateQP <T> a, GenerateQP <T> b)
{
if (a.Fitness > b.Fitness)
{
return(-1);
}
else if (a.Fitness < b.Fitness)
{
return(1);
}
else
{
return(0);
}
}
private void CalculateFitness()
{
fitnessSum = 0;
GenerateQP <T> best = Population[0];
for (int i = 0; i < Population.Count; i++)
{
fitnessSum += Population[i].CalculateFitness(i);
if (Population[i].Fitness > best.Fitness)
{
best = Population[i];
}
}
BestFitness = best.Fitness;
best.Genes.CopyTo(BestGenes, 0);
}
private void CalculateFitness()
{
fitnessSum = 0;
GenerateQP <T> best = Population[0];
foreach (GenerateQP <T> gene in Population)
{
fitnessSum += gene.CalculateFitness();
if (gene.Fitness > best.Fitness)
{
best = gene;
}
}
BestFitness = best.Fitness;
best.Genes.CopyTo(BestGenes, 0);
}
public GeneticAlgorithm(int populationSize, int dnaSize, Random random, Func <int, T> getRandomGene, Func <GenerateQP <T>, float> fitnessFunction,
int elitism, float mutationRate = 0.01f)
{
popsize = populationSize;
Generation = 1;
Elitism = elitism;
MutationRate = mutationRate;
Population = new List <GenerateQP <T> >();
newPopulation = new List <GenerateQP <T> >();
this.random = random;
this.dnaSize = dnaSize;
this.getRandomGene = getRandomGene;
this.fitnessFunction = fitnessFunction;
BestGenes = new T[dnaSize];
for (int i = 0; i < popsize; i++)
{
GenerateQP <T> generateclone = new GenerateQP <T>(dnaSize, random, getRandomGene, fitnessFunction, shouldInitGenes: true);
Population.Add((GenerateQP <T>)generateclone.Clone());
}
CalculateFitness();
}
