// Shows the best path
public void outputBest()
{
Variables.paused = true;
string s = "Best path: " + Variables.bestPopulation[0].Genome.write() +
" with a rating of: " + Variables.bestPopulation[0].Rating.ToString() +
" in: " + elapsedTime;
Output output = new Output(s);
output.Show();
Application.DoEvents();
}
private void button_evaluation_Click(object sender, EventArgs e)
{
for (int i = 0; i < Variables.evaluationsize; i++)
{
GeneticAlgorithm ga = new GeneticAlgorithm(this.glControl1, this.textBox3, this.bar_algorithm_progress);
ga.gaMain();
}
string s = "";
for (int i = 0; i < Variables.evaluationsize; i++)
{
/**s += "Best path: " + Variables.bestPopulation[i].Genome.write() +
" with a rating of: " + Variables.bestPopulation[i].Rating.ToString() +
" in: " + Variables.time[i] + Environment.NewLine;
* **/
s += Variables.bestPopulation[i].Rating.ToString() + " in: " + Variables.time[i] + Environment.NewLine;
}
Output output = new Output(s);
output.Show();
}
// This function opens a new window containing the current population and an option to save all populations to a text file
public void output()
{
Variables.paused = true;
string s = "";
for (int i = 0; i < populationSize; i++)
{
s += i + " : " + "Selected: " + population[i].Selected + " , Mutated: " + population[i].Mutated + " , Rating: " + population[i].Rating
+ " = Distances: " + population[i].Distances + " + Collisions: " + population[i].Collisions + System.Environment.NewLine;
}
Output output = new Output(s);
output.Show();
Application.DoEvents();
// Deactivates after first Generation
// Variables.popDebugging = false;
}
