private void ContinueButton2_Click(object sender, EventArgs e)
{
PopSizeBox.Enabled = false;
GenNumberBox.Enabled = false;
if (AdditionalRadioButton.Checked == true)
{
TimeBox.Enabled = false;
ImprovementBox.Enabled = false;
}
CrossingNumeric.Enabled = false;
MutProbNumeric.Enabled = false;
ContinueButton2.Enabled = false;
ContinueButton3.Enabled = false;
ReturnButton1.Enabled = false;
int pop_size = Int32.Parse(PopSizeBox.Text);
int gens = Int32.Parse(GenNumberBox.Text);
int time = 0;
if (TimeBox.Text != "" || TimeBox.Text != "0")
{
time = Int32.Parse(TimeBox.Text);
}
int iterations = 0;
if (ImprovementBox.Text != "" || ImprovementBox.Text != "0")
{
iterations = Int32.Parse(ImprovementBox.Text);
}
int tournament_size = Int32.Parse(TournamentBox.Text);
double cross_prob = Convert.ToDouble(Math.Round(CrossingNumeric.Value, 0)) / 100;
double mut_prob = Convert.ToDouble(Math.Round(MutProbNumeric.Value, 0)) / 100;
genetic_algorithm = new GA(Instance.Copy(), pop_size, gens, time, iterations, tournament_size, cross_prob, mut_prob);
progressBar1.Minimum = 0;
progressBar1.Value = 0;
progressBar1.Maximum = gens - 1;
ProgressChart.Series["Funkcja celu"].Points.Clear();
genetic_algorithm.OnProgressUpdate += update_form;
bw = new BackgroundWorker();
bw.WorkerReportsProgress = true;
bw.DoWork += new DoWorkEventHandler(
delegate(object o, DoWorkEventArgs args)
{
BackgroundWorker b = o as BackgroundWorker;
GA ga = args.Argument as GA;
ga.life_uh_finds_a_way();
}
);
bw.RunWorkerCompleted += new RunWorkerCompletedEventHandler(
delegate(object o, RunWorkerCompletedEventArgs args)
{
MessageBox.Show("Skończone!!! NAJLEPSZE ROZWIĄZANIE: " + genetic_algorithm.Best.Fitness);
ContinueButton3.Enabled = true;
ReturnButton1.Enabled = true;
PopSizeBox.Enabled = true;
GenNumberBox.Enabled = true;
if (AdditionalRadioButton.Checked == true)
{
TimeBox.Enabled = true;
ImprovementBox.Enabled = true;
}
CrossingNumeric.Enabled = true;
MutProbNumeric.Enabled = true;
ContinueButton2.Enabled = true;
pause = false;
PauseButton.Text = "Pauza";
stop = false;
});
bw.RunWorkerAsync(genetic_algorithm);
PauseButton.Enabled = true;
StopButton.Enabled = true;
}
public void run_tests()
{
string current_dir = @"C:\Users\Jędrzej\Desktop\Studium_Naturae\Zaawansowane_Programowanie\ZP_GA\ZP_GA";
string file_path = current_dir + Path.DirectorySeparatorChar + "Testy" + Path.DirectorySeparatorChar + "testy.csv";
string header = "Fragments;Samples;Fill;Errors;Pop_Size;Gens;Tour_Size;CO;Mut;No;Fitness;Time[s]";
using (StreamWriter sw = new StreamWriter(file_path, true))
sw.WriteLine(header);
//for (int m = 0; m < fragments.Count; m++)
for (int ms = 0; ms < matrix_sizes.Count; ms++) //for (int n = 0; n < samples.Count; n++)
{
int current_fragments = matrix_sizes[ms].Item1; //fragments[m];
int current_samples = matrix_sizes[ms].Item2; //samples[n];
for (int f = 0; f < fills.Count; f++)
{
double current_fill = fills[f];
if (current_fill == 0.2 || current_fill == 0.4)
{
continue;
}
if (current_fill == 0.1 && current_fragments == 10 && current_samples == 10) // Bo 0.1 z 10 to 1 a to każda poprawna
{
continue;
}
List <int> errors = new List <int>();
int max_errors = (current_fragments * current_samples) / 5;
float error_increment = max_errors / 5;
float number_of_errors = 0;
errors.Add(Convert.ToInt32(number_of_errors));
for (int inc = 0; inc < 5; inc++)
{
number_of_errors += error_increment;
errors.Add(Convert.ToInt32(number_of_errors));
}
for (int e = 0; e < errors.Count; e++)
{
for (int p = 0; p < pop_sizes.Count; p++)
{
if (pop_sizes[p] == 150 || pop_sizes[p] == 250 || pop_sizes[p] == 350 || pop_sizes[p] == 450)
{
continue;
}
if (pop_sizes[p] <= current_samples * 10)
{
int current_errors = errors[e];
int pop_size = pop_sizes[p];
List <int> tournament_sizes = new List <int> {
Convert.ToInt32(pop_size * 0.1),
Convert.ToInt32(pop_size * 0.3),
Convert.ToInt32(pop_size * 0.5)
};
for (int g = 0; g < gens.Count; g++)
{
if (gens[g] <= (current_fragments + current_samples) * 10)
{
for (int t = 0; t < tournament_sizes.Count; t++)
{
for (int cv = 0; cv < crossing_overs.Count; cv++)
{
for (int mut = 0; mut < mutations.Count; mut++)
{
int current_gens = gens[g];
int current_tournament = tournament_sizes[t];
double current_cv = crossing_overs[cv];
double current_mut = mutations[mut];
string line_template = "{0};{1};{2};{3};{4};{5};{6};{7};{8};";
string line = string.Format(line_template, current_fragments, current_samples, current_fill, current_errors, pop_size, current_gens, current_tournament, current_cv, current_mut);
Console.WriteLine("\n\nTworzę plik!!! PARAMETRY:\nFragmenty:" + current_fragments + " Próbki: " + current_samples + " FILL: " + current_fill + " ERRORS: " + current_errors + " POP: " + pop_size + " GENS: " + current_gens + " TOURNAMENT: " + current_tournament + " CO: " + current_cv + " MUT:" + current_mut);
for (int i = 0; i < 10; i++)
{
DataTable new_instance = Generator.Create(current_fragments, current_samples, current_fill, current_errors).Instance.Copy();
Stopwatch stopwatch = Stopwatch.StartNew();
GA ga = new GA(new_instance.Copy(), pop_size, current_gens, 0, 0, current_tournament, current_cv, current_mut);
ga.life_uh_finds_a_way();
stopwatch.Stop();
var time = stopwatch.ElapsedMilliseconds;
Console.WriteLine("INSTANCJA: " + i + "; WARTOŚĆ: " + ga.Best.Fitness + " CZAS W SEKUNDACH: " + time * 0.001);
using (StreamWriter sw = new StreamWriter(file_path, true))
sw.WriteLine(line + i + ";" + ga.Best.Fitness + ";" + time * 0.001);
}
}
}
}
}
}
}
}
}
}
}
}