public void Execute()
{
for (int generation = 0; generation < totalGeneration; generation++)
{
double[] fObj = new double[chromosomes.Length];
double[] fitnesses = new double[chromosomes.Length];
double[] probabilities = new double[chromosomes.Length];
double[] cumulativeProbabilities = new double[chromosomes.Length];
double[] randoms = new double[chromosomes.Length];
double totalFitness = 0;
Print($"Initialize Generation #{generation+1}", tag: "h2");
PrintChromosomes(chromosomes);
Print();
// Evaluation
Print("Evaluation", tag: "h3");
for (int i = 0; i < chromosomes.Length; i++)
{
//Console.WriteLine($"Chromosome[{i+1}]: {chromosomes[i]}");
fObj[i] = ObjectiveFunction.Invoke(chromosomes[i]);
fitnesses[i] = 1 / (1 + fObj[i]);
Print($"F_obj[{i + 1}] = {fObj[i]}");
totalFitness += fitnesses[i];
}
//Fitness
for (int i = 0; i < fitnesses.Length; i++)
{
Print($"Fitness[{i + 1}] = {fitnesses[i]}");
}
Print($"Total fitness = {totalFitness}");
Print();
Random random = new Random();
Print("Probabilities");
for (int i = 0; i < chromosomes.Length; i++)
{
probabilities[i] = fitnesses[i] / totalFitness;
Print($"P[{i + 1}]: {probabilities[i]}");
cumulativeProbabilities[i] = probabilities.Sum();
randoms[i] = random.NextDouble();
//Console.WriteLine($"C[{i + 1}]{cumulativeProbabilities[i]}");
}
Print();
Print("Cumulative probabilities");
for (int i = 0; i < chromosomes.Length; i++)
{
Print($"C[{i + 1}]: {cumulativeProbabilities[i]}");
}
Print();
Print("Roulette-wheel", tag: "h3");
for (int i = 0; i < chromosomes.Length; i++)
{
Print($"R[{i + 1}]: {randoms[i]}");
}
Print();
Chromosome <T>[] newChromosomes = new Chromosome <T> [chromosomes.Length];
for (int i = 0; i < randoms.Length; i++)
{
for (int j = 0; j < cumulativeProbabilities.Length; j++)
{
if (randoms[i] > cumulativeProbabilities[j])
{
continue;
}
newChromosomes[i] = new Chromosome <T>();
newChromosomes[i].Genes = new T[chromosomes[j].Genes.Length];
chromosomes[j].Genes.CopyTo(newChromosomes[i].Genes, 0);
break;
}
}
Print("New selected chromosomes:", tag: "h3");
PrintChromosomes(newChromosomes);
Print();
// Crossover
int[] selectedIndexes = SelectChromosomes(chromosomes.Length).ToArray();
Print("Crossover", tag: "h3", centered: true);
//Console.WriteLine($"Indexcount:{selectedIndexes.Length}");
if (selectedIndexes.Length > 1)
{
Dictionary <int, T[]> offsprings = new Dictionary <int, T[]>();
for (int i = 0; i < selectedIndexes.Length; i++)
{
int cut = random.Next(1, newChromosomes[selectedIndexes[i]].Genes.Length);
int targeti = i + 1 >= selectedIndexes.Length ? 0 : i + 1;
Print($"Cut:{cut}", centered: true);
Print($"Chromosome[{selectedIndexes[i] + 1}]><Chromosome[{selectedIndexes[targeti] + 1}]", centered: true);
var gene = newChromosomes[selectedIndexes[i]].Genes;
var targetgene = newChromosomes[selectedIndexes[targeti]].Genes;
Print($"[{string.Join(';', gene)}]><[{string.Join(';', targetgene)}]", centered: true);
T[] offspring = new T[gene.Length];
for (int j = 0; j < offspring.Length; j++)
{
offspring[j] = gene[j];
if (j >= cut)
{
offspring[j] = targetgene[j];
}
}
Print($"[{string.Join(';', offspring)}]", centered: true);
Print();
offsprings.Add(selectedIndexes[i], offspring);
}
Print();
foreach (var item in offsprings)
{
item.Value.CopyTo(newChromosomes[item.Key].Genes, 0);
}
Print("New Generation Chromosomes");
PrintChromosomes(newChromosomes);
}
else
{
Print("No crossover");
}
Print();
// Mutation
Print("Mutation", tag: "h3");
(int x, int y)[] selectedGenes = SelectGeneForMutation(newChromosomes.Length, newChromosomes[0].Genes.Length).ToArray();