public void Train(int iterations = 1)
{
while (iterations-- > 0)
{
List<Species> parents = Population;
Population = new List<Species>(PopulationSize);
Population.Add(parents.First());
//Tournament Selection
while (Population.Count < PopulationSize)
{
Species[] pair = new Species[2];
for (int i = 0; i < 2; i++)
{
int[] randomIndices = new int[10];
for (int j = 0; j < randomIndices.Length; j++)
randomIndices[j] = int.MaxValue;
for (int j = 0; j < randomIndices.Length; j++)
{
int r = R.NextNot(parents.Count, randomIndices);
randomIndices[j] = r;
}
List<Species> tournament = new List<Species>(randomIndices.Length);
for (int j = 0; j < randomIndices.Length; j++)
tournament.Add(parents[randomIndices[j]]);
pair[i] = tournament.OrderByDescending(s => s.Fitness).First();
}
//now have two parents.
Species sp = ~(pair[0] ^ pair[1]);
sp.Clamp();
Population.Add(sp);
}
List<Task> tasks = new List<Task>();
foreach (Species s in Population)
{
int pidd = pid++;
Task t = new Task(() =>
{
s.Fitness = FitnessFunction(s.Genome, pidd, true);
});
tasks.Add(t);
t.Start();
}
tasks.ForEach(t => t.Wait());
Population.Sort((s, t) => t.Fitness.CompareTo(s.Fitness));
/* Console.Clear();
Console.WriteLine(BestIndividual.Fitness);*/
FitnessFunction(BestIndividual.Genome, -iterations, false);
}
}
public static Species operator ^(Species a, Species b)
{
if (a.Genome.Length != b.Genome.Length)
throw new ArgumentException("Can't crossover species of different genome length");
Species result = new Species(a);
int cut = R.Next(a.Genome.Length + 1);
for (int i = cut; i < a.Genome.Length; i++)
result.Genome[i] = b.Genome[i];
return result;
}
public static Species operator ~(Species a)
{
Species result = new Species(a);
const double scale = 10;
for (int i = 0; i < a.Genome.Length; i++)
if (R.NextDouble() < 0.4)
a.Genome[i] = Math.Abs(a.Genome[i] + R.NextDouble() * 2 * scale - scale);
return result;
}
public static Species operator -(Species a)
{
Species result = new Species(a);
for (int i = 0; i < a.Genome.Length; i++)
result.Genome[i] = -a.Genome[i];
return result;
}
//Vector operations:
public static Species operator +(Species a, Species b)
{
if (a.Genome.Length != b.Genome.Length)
throw new ArgumentException("Can't vector add species of different genome length");
Species result = new Species(a);
for (int i = 0; i < a.Genome.Length; i++)
result.Genome[i] += b.Genome[i];
return result;
}
public static Species operator *(double c, Species a)
{
Species result = new Species(a);
for (int i = 0; i < a.Genome.Length; i++)
result.Genome[i] *= c;
return result;
}
public Species(Species s)
{
Genome = new double[s.Genome.Length];
for (int i = 0; i < s.Genome.Length; i++)
Genome[i] = s.Genome[i];
}
public void Train(int iterations = 1)
{
const double beta = 0.5;
const double crossoverRate = 0.5;
while (iterations-- > 0)
{
List<Species> newPop = new List<Species>(PopulationSize);
List<Task> tasks = new List<Task>();
for (int i = 0; i < PopulationSize; i++)
{
int ppid = pid++;
int actuali = i;
Task t = new Task(() =>
{
Species xnew;
Species x;
lock (Population)
{
x = Population[actuali];
int i1 = R.NextNot(PopulationSize, actuali);
int i2 = R.NextNot(PopulationSize, actuali, i1);
int i3 = R.NextNot(PopulationSize, actuali, i1, i2);
Species x1 = Population[i1];
Species x2 = Population[i2];
Species x3 = Population[i3];
Species u = x1 + beta * (x2 - x3);
u.Clamp();
xnew = new Species(x);
for (int j = 0; j < GenomeLength; j++)
if (R.NextDouble() < crossoverRate)
xnew.Genome[j] = u.Genome[j];
}
xnew.Fitness = FitnessFunction(xnew.Genome, ppid, true);
lock (newPop)
{
if (x.Fitness < xnew.Fitness)
newPop.Add(xnew);
else newPop.Add(x);
}
});
tasks.Add(t);
t.Start();
}
tasks.ForEach(t => t.Wait());
Population = newPop;
Population.Sort((x, y) => y.Fitness.CompareTo(x.Fitness));
Console.Clear();
Console.WriteLine(BestIndividual.Fitness);
FitnessFunction(BestIndividual.Genome, -iterations, false);
}
}