void PerformGeneticAlgorithm()
{
BitmapResult expectedResult = new BitmapResult(SourceImage);
int generationsCount = 200;
for (int i = 1; i < generationsCount; i++)
{
Generation.CalculateResults();
Generation.CalculateFitness(expectedResult);
Generation.NaturalSelection(100);
this.Invoke(new MethodInvoker(delegate ()
{
int perc = i * 100 / generationsCount;
txtLog.Text = "GEN " + i + " (" + perc + "%)\r\n";
foreach (var o in Generation.Population)
{
txtLog.Text += o.Fitness + "\r\n";
}
DrawCurrentBest(Generation.Population[0]);
Generation.Population[0].GetResult().Save("c:/devel/gen/" + i.ToString("d5") + ".jpg");
}));
}
GeneticAlgorithm.Abort();
}
private void btnCalculateFitness_Click(object sender, EventArgs e)
{
DrawSourceImage();
BitmapResult expectedResult = new BitmapResult(SourceImage);
Generation.CalculateResults();
Generation.CalculateFitness(expectedResult);
foreach (var o in Generation.Population)
{
txtLog.Text += o.Fitness + "\r\n";
}
}
public static void SendResponse(this HttpListenerContext context, BitmapResult result)
{
if (result.Bitmap != null)
{
using (var memoryStream = new MemoryStream())
{
result.Bitmap.Save(memoryStream, ImageFormat.Png);
memoryStream.WriteTo(context.Response.OutputStream);
}
result.Bitmap.Dispose();
}
context.SendResponse(result.StatusCode);
}
