// Use this for initialization
void Start()
{
genNOindicator = GetComponentInChildren <Text> ();
ptr = -1;
finished = false;
popSIze = 100;
Debug.Log("starting");
startObj = new population(popSIze);
bestgenesholder1 = new List <int[]> ();
bestgenesholder1 = startObj.bestgenesholder;
startInatantiating();
}
//void Start
void Start()
{
inputsTO_NN = new float[InputCountANN];
layers = new int[] { InputCountANN, 9, 9, 8 };
net = new NeuralNetwork(layers);
string[] g = net.GetAllWeightNN();
float[] DF = new float[7];
DF[0] = 0; DF[1] = 100; DF[2] = 0; DF[3] = 0; DF[4] = 0; DF[5] = 0; DF[6] = 0;
//creamos nueva pobacion y pasamos los pesos de la ANN y los datos para el calculo de fitness
p = new population(g, DF);
}
//Мутация
public void Mutation(population parent)
{
double Pm = 2 / (numberOfIndividuals * 1.0);
for (int i = 0; i < parent.Population.Count; i++)
{
for (int j = 0; j < parent.Population[i].Genes.Count(); j++)
{
double p = random.NextDouble();
if (Pm > p)
{
parent.Population[i].Genes[j] += random.Next(-5, 11);
}
}
}
}
//отбор при помощи турнинрой селекции с турниром (размер турнира в переменной)
public void choice(population parent, population selectionparent)
{
Random rand = new Random();
population parentForSelection = new population();
for (int i = 0; i < numberOfIndividuals / 2; i++)
{
int theBest = 0;
double theBestresult = 50000;
for (int j = 0; j < sizeTourney; j++)
{
int in1 = rand.Next(0, numberOfIndividuals);
if (absolutlifitnessPersent(parent.Population[in1], model) < theBestresult)
{
theBestresult = absolutlifitnessPersent(parent.Population[in1], model);
theBest = in1;
}
}
parentForSelection.Population.Add(parent.Population[theBest]);
selectionparent.Population.Add(parent.Population[theBest]);
}
parent.Population.Clear();
individual child1;
individual child2;
while (parent.Population.Count < numberOfIndividuals)
{
int i = rand.Next(0, numberOfIndividuals / 2);
int j = rand.Next(0, numberOfIndividuals / 2);
if (p > rand.NextDouble())
{
crossingover(parentForSelection.Population[i], parentForSelection.Population[j], child1 = new individual(), child2 = new individual());
}
else
{
child1 = new individual();
child2 = new individual();
child1.Genes = parentForSelection.Population[i].Genes;
child2.Genes = parentForSelection.Population[j].Genes;
}
parent.Population.Add(child1);
parent.Population.Add(child2);
}
}
RunGA(population, iterations, Crosseover, crossoverProbability, fitness, mutation, selection);
private void button_win_Click(object sender, EventArgs e)
{
//данные с формы
numberOfIndividuals = Convert.ToInt32(PopulationNumber.Text);
sizeTourney = Convert.ToInt32(textBox_sizeTourney.Text);
//храним родителей, родителей для селекции и потомство
parents = new population();
parentsForSelection = new population();
children = new population();
Random rnd = new Random();
//Эталонная особь
model.Genes = new int[] { 96, 96, 159 };
//Генерация начальной популяции
population FirstPopulation = new population();
for (int i = 0; i < numberOfIndividuals; i++)
{
individual curentIndividuals = new individual();
curentIndividuals.Genes = new int[] { rnd.Next(0, 256), rnd.Next(0, 256), rnd.Next(0, 256) };
FirstPopulation.Population.Add(curentIndividuals);
}
bool individualIsfind = false;
int iter = 0;
listBox_number.Items.Clear();
while (!individualIsfind)
{
double theBestreslt = 50000;
for (int i = 0; i < FirstPopulation.Population.Count; i++)
{
double rez = absolutlifitnessPersent(FirstPopulation.Population[i], model);
if (rez < theBestreslt)
{
theBestreslt = rez;
}
if (absolutlifitnessPersent(FirstPopulation.Population[i], model) == 0)
{
individualIsfind = true;
int number = i;
break;
}
}
rezult.Add(theBestreslt);
if (!individualIsfind)
{
iter++;
foreach (var item in FirstPopulation.Population)
{
parents.Population.Add(item);
}
listBox_number.Items.Add(iter);
choice(FirstPopulation, parentsForSelection);
foreach (var item in FirstPopulation.Population)
{
children.Population.Add(item);
}
Mutation(FirstPopulation);
}
}
Form2 graph = new Form2(rezult, iter);
graph.Show();
}
