public void CalculateDifferences(GeneticPopulation population, GeneticBlackBox blackBox)
{
foreach (GeneticSample sample in population.Samples)
{
sample.DifferenceUnits = blackBox.GetDifferenceUnits(sample.Color);
sample.DifferencePercents = blackBox.GetDifferencePercents(sample.Color);
sample.SimilarityUnits = blackBox.GetSimilarityUnits(sample.Color);
sample.SimilarityPercents = blackBox.GetSimilarityPercents(sample.Color);
}
}
private void AddPopulation(GeneticPopulation population)
{
dgvPopulations.Rows.Add
(
dgvPopulations.Rows.Count + 1,
GetEliteImage(population),
string.Concat(population.Elite.Color.Red, " ",
population.Elite.Color.Green, " ",
population.Elite.Color.Blue),
population.Elite.DifferenceUnits,
population.Elite.SimilarityPercents,
GetPopulationImage(population)
);
}
private void _algorythm_PopulationCalculated(byte progressPercents, GeneticPopulation population)
{
if (this.InvokeRequired)
this.Invoke(new GeneticAlgorithm.PopulationDelegate(_algorythm_PopulationCalculated),
progressPercents, population);
else
{
pbProgress.Value = progressPercents;
AddPopulation(population);
}
}
private void _algorythm_CalculationSucess(byte progressPercents, GeneticPopulation population)
{
if (this.InvokeRequired)
this.Invoke(new GeneticAlgorithm.PopulationDelegate(_algorythm_CalculationSucess),
progressPercents, population);
else
{
ShowResults(population);
ToggleFromControls(true);
if (_closeFlag)
this.Close();
}
}
private void ShowResults(GeneticPopulation population)
{
var color = population.Elite.Color;
btnEliteColor.BackColor = Color.FromArgb(byte.MaxValue,
color.Red, color.Green, color.Blue);
tbxEliteValue.Text = string.Concat(color.Red, " ",
color.Green, " ", color.Blue);
if (color.Red == btnTarget.BackColor.R &&
color.Green == btnTarget.BackColor.G &&
color.Blue == btnTarget.BackColor.B)
{
tbxResult.Text = @"SUCCESS";
}
else
{
tbxResult.Text = string.Format("{0} %",
population.Elite.SimilarityPercents);
}
}
private Image GetPopulationImage(GeneticPopulation population)
{
const int size = 10;
const int w = size - 1;
var bmp = new Bitmap(size * population.Samples.Count, size);
var g = Graphics.FromImage(bmp);
for (int i = 0; i < population.Samples.Count; i++)
{
var color = Color.FromArgb(byte.MaxValue,
population.Samples[i].Color.Red,
population.Samples[i].Color.Green,
population.Samples[i].Color.Blue);
g.FillRectangle(new SolidBrush(color), i * size, 0, size, w);
g.DrawRectangle(Pens.Black, i * size, 0, size, w);
}
g.DrawLine(Pens.Black, population.Samples.Count * size - 1, 0, population.Samples.Count * size - 1, size);
return bmp;
}
private Image GetEliteImage(GeneticPopulation population)
{
const int sizex = 45, sizey = 15;
const int w = sizex - 1, h = sizey - 1;
var bmp = new Bitmap(sizex, sizey);
var g = Graphics.FromImage(bmp);
var srcColor = population.Elite.Color;
var color = Color.FromArgb(byte.MaxValue,
srcColor.Red,
srcColor.Green,
srcColor.Blue);
g.FillRectangle(new SolidBrush(color), 0, 0, w, h);
g.DrawRectangle(Pens.Black, 0, 0, w, h);
return bmp;
}
public void CrossPopulation(GeneticPopulation population)
{
var count = population.Samples.Count;
var restCount = Convert.ToInt32(0.01 * count * _settings.Elitism);
if (restCount < 1)
restCount = 1;
var removeCount = count - restCount;
var minDeviation = Convert.ToInt32(0.01*byte.MaxValue*_settings.MutabilityMin);
var maxDeviation = Convert.ToInt32(0.01*byte.MaxValue*_settings.MutabilityMax)+1;
population.Samples.RemoveRange(restCount, removeCount);
for (var i = restCount; i < count; i++)
{
var parent1Index = _random.Next(0, restCount);
var parent2Index = _random.Next(0, restCount);
var parent1Percentage = Convert.ToByte(_random.Next(0, 101));
var childColor = population.Samples[parent1Index].Color.Blend(
population.Samples[parent2Index].Color, parent1Percentage);
if (_random.Next(0, 101) < _settings.Mutation)
{
childColor.Red += Convert.ToByte(_random.Next(minDeviation, maxDeviation));
childColor.Green += Convert.ToByte(_random.Next(minDeviation, maxDeviation));
childColor.Blue += Convert.ToByte(_random.Next(minDeviation, maxDeviation));
}
var child = new GeneticSample
{
Color = childColor,
DifferenceUnits = 0,
DifferencePercents = 0,
SimilarityPercents = 0,
SimilarityUnits = 0,
Age = 0
};
population.Samples.Add(child);
}
for(var i=0; i<restCount;i++)
{
population.Samples[i].Age++;
if (population.Samples[i].Age>=_settings.Age)
{
population.Samples[i] = GeneticSample.GetRandomGeneticSample(_random);
}
}
}
private void InitPopulation(out GeneticPopulation population)
{
population = new GeneticPopulation();
for (var i=0;i<_settings.Size;i++)
{
var sample = GeneticSample.GetRandomGeneticSample(_random);
population.Samples.Add(sample);
}
}
public void SortPopulation(GeneticPopulation population)
{
var samples = new List<GeneticSample>();
population.Samples.Sort(new Comparison<GeneticSample>(
(s1, s2) => s1.DifferenceUnits.CompareTo(s2.DifferenceUnits)));
var samplesByDifference = population.Samples.GroupBy(s => s.DifferenceUnits);
foreach (IGrouping<int, GeneticSample> samplesGroup in samplesByDifference)
{
var samplesList = samplesGroup.ToList();
samplesList.Sort((s1, s2) => s2.Age.CompareTo(s1.Age));
samples.AddRange(samplesList);
}
population.Samples = samples;
}
