public void Genetic_Algorithm_With_Order_Initialization()
{
// Parent Generation - initial Generation
Generation generation = Initialization_With_Order();
Evaluate_Population(generation);
GenerationCollection.Add(generation);
// Childs Generation
int Individual_1 = 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);
New_Individual = GenerationCollection[GenerationCollection.Count - 1].People.ElementAt(Individual_1);
New_Individual = Mutation(New_Individual);
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);
Initialization_Order_Fitness.Add(NewGeneration.People[0].fitness);
generation = NewGeneration;
if (NewGeneration.People[0].fitness <= 0)
{
break;
}
}
}
public void Genetic_Algorithm_With_RandomInitalization()
{
Individual Random_individual;
// Parent Generation
Generation generation = Initialization();
Evaluate_Population(generation);
GenerationCollection.Add(generation);
// Childs Generations
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);
Initialization_Random_Fitness.Add(NewGeneration.People[0].fitness);
generation = NewGeneration;
if (NewGeneration.People[0].fitness <= 0)
{
break;
}
}
}
public void Genetic_Algorithm()
{
Individual Random_individual;
// Parent Generation
Generation generation = Initialization();
Evaluate_Population(generation);
GenerationCollection.Add(generation);
// Childs Generations
int Individual_1 = 0;
int Individual_2 = 0;
Individual New_Individual;
Generation NewGeneration;
int NO_Generation = 0;
int Draw_Each_Num_Generation = Convert.ToInt32(ProgramSettings.Default.DrawEachNumGeneration);
int Free_in_Generation = 0;
if (MainForm.Rectangles.Count < 50)
{
Free_in_Generation = 1000;
}
else if (MainForm.Rectangles.Count < 100)
{
Free_in_Generation = 500;
}
else if (MainForm.Rectangles.Count < 200)
{
Free_in_Generation = 300;
}
else if (MainForm.Rectangles.Count < 400)
{
Free_in_Generation = 100;
}
else if (MainForm.Rectangles.Count < 1000)
{
Free_in_Generation = 75;
}
else
{
Free_in_Generation = 30;
}
while (true)
{
IS_Finished = false;
NO_Generation++;
int GenFree = 0;
if (NO_Generation % Free_in_Generation == 0)
{
for (int i = GenFree; i < GenerationCollection.Count - 2; i++)
{
for (int j = 1; j < GenerationCollection[i].People.Count; j++)
{
for (int k = 0; k < GenerationCollection[i].People[j].chromosome.Count; k++)
{
GenerationCollection[i].People[j].chromosome[k] = null;
GenerationCollection[i].People[j].chromosome.RemoveAt(k);
k = 0;
}
GenerationCollection[i].People[j] = null;
GenerationCollection[i].People.RemoveAt(j);
j = 1;
}
}
GenFree += 1000;
GC.Collect();
}
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;
}
totalFitness = 0;
for (int i = 0; i < generation.People.Count; i++)
{
totalFitness += generation.People[i].fitness;
}
for (int i = 0; i < NumOfIndividualToUpdate; i++)
{
Individual_1 = RouletteWheelSelection(totalFitness, generation);
Individual_2 = RouletteWheelSelection(totalFitness, generation);
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;
}
if (rand.NextDouble() < 0.8)
{
New_Individual = Mutation(New_Individual);
}
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);
if (EnterNewIndividualcheckBox.Checked)
{
int sizeofNew = (20 * Convert.ToInt32(ProgramSettings.Default.PopulationSize)) / 100;
NewGeneration.People.RemoveRange(NewGeneration.People.Count - 1 - sizeofNew, sizeofNew);
Individual Randomindividual = null;
for (int i = 0; i < sizeofNew; i++)
{
Randomindividual = new Individual();
Randomindividual.chromosome = NewGeneration.People[0].chromosome.OrderBy(x => rand.Next()).ToList();
Randomindividual.fitness = CalcFitness(Randomindividual);
New_Individual = (Individual)Randomindividual.Clone();
NewGeneration.People.Insert(NewGeneration.People.Count - 1, New_Individual);
Random_individual = null;
}
SortByFitness(NewGeneration);
}
GenerationCollection.Add(NewGeneration);
generation = NewGeneration;
MethodInvoker action = delegate
{
numberGenerationLabel.Text = NO_Generation.ToString();
bestFitnesslabel.Text = (Math.Round((GenerationCollection[GenerationCollection.Count - 1].People[0].fitness + Area_MainRectangle) / Area_MainRectangle, 3)).ToString() + " %" + " - " + GenerationCollection[GenerationCollection.Count - 1].People[0].fitness;
};
numberGenerationLabel.BeginInvoke(action);
Draw_Each_Num_Generation = Convert.ToInt32(ProgramSettings.Default.DrawEachNumGeneration);
if (NO_Generation % Draw_Each_Num_Generation == 0)
{
MethodInvoker action2 = delegate
{
Draw_Packing(NewGeneration.People[0]);
};
PictureBoxDraw.BeginInvoke(action2);
}
if (NewGeneration.People[0].fitness <= 0)
{
break;
}
}
IS_Finished = true;
MethodInvoker action3 = delegate
{
numberGenerationLabel.Text = NO_Generation.ToString();
PlayPausetoolStripButton.Checked = false;
toolStripProgressBar1.Visible = false;
toolStripStatusLabel1.Visible = false;
toolStripDropDownButton1.Enabled = true;
DrawCharttoolStripButton.Enabled = true;
BestPolishtoolStripButton.Enabled = true;
CrossoverMutationtoolStripButton.Enabled = true;
Compare2toolStripButton.Enabled = true;
MainForm.stopWatch.Stop();
TimeSpan ts = MainForm.stopWatch.Elapsed;
TimeElapsedlabel.Text = String.Format("{0:00}:{1:00}:{2:00}.{3:00}",
ts.Hours, ts.Minutes, ts.Seconds,
ts.Milliseconds / 10);
Draw_Packing(GenerationCollection[GenerationCollection.Count - 1].People[0]);
};
numberGenerationLabel.BeginInvoke(action3);
MainForm.oThread.Abort();
}
