public Point(OptimizationModel optModel, double x1, double x2)
{
Accuracy = optModel.Accuracy;
//////////Доделать округление
X1 = x1;
X2 = x2;
}
private void button1_Click(object sender, EventArgs e)
{
richTextBox1.Text = "";
desc.Clear();
alg = new PGA();
OptimizationModel optModel = new OptimizationModel(func);
AlgorithmSettings settings = new AlgorithmSettings()
{
InitialLoadType = (InitialLoadType)comboBox1.SelectedItem,
OptModel = optModel,
InitialPointCount = (int)numericUpDown1.Value,
SelectionType = (SelectionType)comboBox2.SelectedItem,
EndCondition = (EndCondition)comboBox3.SelectedItem,
MaxGenerationCount = (int)numericUpDown2.Value,
SurvivedCount = (int)numericUpDown3.Value,
MutationChance = (double)numericUpDown4.Value,
CrossingGenNumber = (int)numericUpDown5.Value,
Tolerance = (double)numericUpDown6.Value,
MutationChanceAfterCrossing = (double)numericUpDown7.Value,
MutationType = (MutationType)comboBox4.SelectedItem
};
alg.Run(settings);
DrawRezult(alg);
WriteRezult(alg);
}
public Point(OptimizationModel optModel,double x1,double x2)
{
Accuracy = optModel.Accuracy;
//////////Доделать округление
X1 = x1;
X2 = x2;
}
public Chromosome(OptimizationModel optModel, bool[] genes)
{
m_OptModel = optModel;
m_Genes = new bool[14];
for (int i = 0; i < 14; i++)
{
m_Genes[i] = genes[i];
}
Id = identity;
identity++;
}
public Chromosome(OptimizationModel optModel, bool[] genes)
{
m_OptModel = optModel;
m_Genes = new bool[14];
for (int i = 0; i < 14; i++)
{
m_Genes[i] = genes[i];
}
Id = identity;
identity++;
}
private void backgroundWorker1_DoWork(object sender, DoWorkEventArgs e)
{
int progress = 0;
optrez.Clear();
finalRez.Clear();
alg = new PGA();
OptimizationModel optModel = new OptimizationModel(func);
EnumConverter InitialLoadTypeCollection = new EnumConverter(typeof(InitialLoadType));
EnumConverter EndConditionTypeCollecton = new EnumConverter(typeof(EndCondition));
EnumConverter MutationTypeCollecton = new EnumConverter(typeof(MutationType));
EnumConverter SelectionTypeCollection = new EnumConverter(typeof(SelectionType));
foreach (InitialLoadType il in InitialLoadTypeCollection.GetStandardValues())
{
foreach (EndCondition ec in EndConditionTypeCollecton.GetStandardValues())
{
foreach (MutationType mt in MutationTypeCollecton.GetStandardValues())
{
foreach (SelectionType st in SelectionTypeCollection.GetStandardValues())
{
double[] f1m = new double[(int)numericUpDown8.Value];
double[] f2m = new double[(int)numericUpDown8.Value];
double[] fm = new double[(int)numericUpDown8.Value];
double[] x1m = new double[(int)numericUpDown8.Value];
double[] x2m = new double[(int)numericUpDown8.Value];
for (int i = 0; i < (int)numericUpDown8.Value; i++)
{
AlgorithmSettings settings = new AlgorithmSettings()
{
InitialLoadType = il,
OptModel = optModel,
InitialPointCount = (int)numericUpDown1.Value,
SelectionType = st,
EndCondition = ec,
MaxGenerationCount = (int)numericUpDown2.Value,
SurvivedCount = (int)numericUpDown3.Value,
MutationChance = (double)numericUpDown4.Value,
CrossingGenNumber = (int)numericUpDown5.Value,
Tolerance = (double)numericUpDown6.Value,
MutationChanceAfterCrossing = (double)numericUpDown7.Value,
MutationType = mt
};
alg.Run(settings);
double x1 = alg.Best.X1;
double x2 = alg.Best.X2;
double f1 = alg.Best.F;
double f2 = alg.CallCount;
double f = GetCriterion(f1, f2);
f1m[i] = f1;
f2m[i] = f2;
fm[i] = f;
x1m[i] = x1;
x2m[i] = x2;
optrez.Add(new OptRezult()
{
I = il,
E = ec,
S = st,
M = mt,
F1 = f1,
F2 = f2,
X1 = x1,
X2 = x2,
F = f
});
}
progress++;
backgroundWorker1.ReportProgress(progress * 100 / 24);
finalRez.Add(new OptRezult()
{
I = il,
E = ec,
S = st,
M = mt,
X1 = x1m.Average(),
X2 = x2m.Average(),
F1 = f1m.Average(),
F2 = f2m.Average(),
F = fm.Average()
});
}
}
}
}
}
public Chromosome(OptimizationModel optModel, double x1, double x2)
{
m_OptModel = optModel;
m_Genes = new bool[14];
for (int i = 0; i < m_Genes.Length; i++)
{
m_Genes[i] = false;
}
if (x1 < 0)
{
m_Genes[0] = true;
}
if (x2 < 0)
{
m_Genes[7] = true;
}
int fx1 = (int)Math.Floor(Math.Abs(x1));
int fx2 = (int)Math.Floor(Math.Abs(x2));
if (fx1 > 10)
{
throw new ArgumentOutOfRangeException("x1", x1, "x1 must be [-10;10]");
}
if (fx1 >= 8)
{
m_Genes[1] = true;
}
if ((fx1 >= 4) && (fx1 <= 7))
{
m_Genes[2] = true;
}
if ((fx1 == 2) || (fx1 == 3) || (fx1 == 6) || (fx1 == 7) || (fx1 == 10))
{
m_Genes[3] = true;
}
if (fx1 % 2 == 1)
{
m_Genes[4] = true;
}
if (fx2 >= 8)
{
m_Genes[8] = true;
}
if ((fx2 >= 4) && (fx2 <= 7))
{
m_Genes[9] = true;
}
if ((fx2 == 2) || (fx2 == 3) || (fx2 == 6) || (fx2 == 7) || (fx2 == 10))
{
m_Genes[10] = true;
}
if (fx2 % 2 == 1)
{
m_Genes[11] = true;
}
double dec1 = Math.Abs(x1) - fx1;
double dec2 = Math.Abs(x2) - fx2;
if ((dec1 > 0.15) && (dec1 < 0.35))
{
m_Genes[6] = true;
}
if ((dec1 > 0.35) && (dec1 < 0.6))
{
m_Genes[5] = true;
}
if (dec1 > 0.6)
{
m_Genes[6] = true;
m_Genes[5] = true;
}
if ((dec2 > 0.15) && (dec2 < 0.35))
{
m_Genes[13] = true;
}
if ((dec2 > 0.35) && (dec2 < 0.6))
{
m_Genes[12] = true;
}
if (dec2 > 0.6)
{
m_Genes[13] = true;
m_Genes[12] = true;
}
Id = identity;
identity++;
}
public Chromosome(OptimizationModel optModel, double x1, double x2)
{
m_OptModel = optModel;
m_Genes = new bool[14];
for (int i = 0; i < m_Genes.Length; i++)
{
m_Genes[i] = false;
}
if (x1 < 0)
m_Genes[0] = true;
if (x2 < 0)
m_Genes[7] = true;
int fx1 = (int)Math.Floor(Math.Abs(x1));
int fx2 = (int)Math.Floor(Math.Abs(x2));
if (fx1 > 10)
throw new ArgumentOutOfRangeException("x1", x1, "x1 must be [-10;10]");
if (fx1 >= 8)
m_Genes[1] = true;
if ((fx1 >= 4) && (fx1 <= 7))
m_Genes[2] = true;
if ((fx1 == 2) || (fx1 == 3) || (fx1 == 6) || (fx1 == 7) || (fx1 == 10))
m_Genes[3] = true;
if (fx1 % 2 == 1)
m_Genes[4] = true;
if (fx2 >= 8)
m_Genes[8] = true;
if ((fx2 >= 4) && (fx2 <= 7))
m_Genes[9] = true;
if ((fx2 == 2) || (fx2 == 3) || (fx2 == 6) || (fx2 == 7) || (fx2 == 10))
m_Genes[10] = true;
if (fx2 % 2 == 1)
m_Genes[11] = true;
double dec1 = Math.Abs(x1) - fx1;
double dec2 = Math.Abs(x2) - fx2;
if ((dec1 > 0.15) && (dec1 < 0.35))
m_Genes[6] = true;
if ((dec1 > 0.35) && (dec1 < 0.6))
m_Genes[5] = true;
if (dec1 > 0.6)
{
m_Genes[6] = true;
m_Genes[5] = true;
}
if ((dec2 > 0.15) && (dec2 < 0.35))
m_Genes[13] = true;
if ((dec2 > 0.35) && (dec2 < 0.6))
m_Genes[12] = true;
if (dec2 > 0.6)
{
m_Genes[13] = true;
m_Genes[12] = true;
}
Id = identity;
identity++;
}
