public void Genetic_Algorithm_CrossOver()
{
Individual Random_individual;
// Parent Generation - initial Generation
Generation generation = Initialization();
Evaluate_Population(generation);
GenerationCollection.Add(generation);
// Childs Generation
int Individual_1 = 0;
int Individual_2 = 0;
Individual New_Individual;
Generation NewGeneration;
for (int n = 0; n < NumberOfGeneration; n++)
{
NewGeneration = (Generation)generation.Clone();
double tempcost = 0;
string msg = "";
for (int i = 0; i < generation.People.Count; i++)
{
tempcost += (double)(1 / generation.People[i].fitness);
generation.People[i].cost = tempcost;
msg += i.ToString() + generation.People[i].cost.ToString() + " " + (1 / generation.People[i].fitness).ToString() + Environment.NewLine;
}
for (int i = 0; i < UpdatePercentage; i++)
{
Individual_1 = RouletteSelection(generation);
Individual_2 = RouletteSelection(generation);
while (Individual_1 == Individual_2)
{
Individual_2 = RouletteSelection(generation);
}
// New_Individual = GenerationCollection[GenerationCollection.Count - 1].People.ElementAt(0);
if (rand.NextDouble() < 0.5)
{
New_Individual = CrossOver(GenerationCollection[GenerationCollection.Count - 1].People.ElementAt(Individual_1), GenerationCollection[GenerationCollection.Count - 1].People.ElementAt(Individual_2));
}
else if (rand.NextDouble() < 0.6)
{
New_Individual = CrossOver2(GenerationCollection[GenerationCollection.Count - 1].People.ElementAt(Individual_1), GenerationCollection[GenerationCollection.Count - 1].People.ElementAt(Individual_2));
}
else
{
New_Individual = GenerationCollection[GenerationCollection.Count - 1].People.ElementAt(0);
Random_individual = new Individual();
Random_individual.chromosome = New_Individual.chromosome.OrderBy(x => rand.Next()).ToList();
Random_individual.fitness = New_Individual.fitness;
New_Individual = (Individual)Random_individual.Clone();
Random_individual = null;
}
NewGeneration.People.ElementAt(generation.People.Count - 1 - i).chromosome = null;
NewGeneration.People.RemoveAt(generation.People.Count - 1 - i);
NewGeneration.People.Insert(generation.People.Count - 1 - i, New_Individual);
New_Individual.fitness = CalcFitness(New_Individual);
}
SortByFitness(NewGeneration);
GenerationCollection.Add(NewGeneration);
Crossover_Fitness.Add(NewGeneration.People[0].fitness);
generation = NewGeneration;
if (NewGeneration.People[0].fitness <= 0)
{
break;
}
}
}
private void SortByFitness(Generation generation)
{
generation.People.Sort(delegate(Individual c1, Individual c2) { return(c1.fitness.CompareTo(c2.fitness)); });
}
