public void Mutate()
{
for (int i = 0; i < _jobsLength; i++)
{
if (SimpleRNG.GetUniform() < _mutationRate)
{
int r = SimpleRNG.Next(0, _jobsLength);
int temp = JobGenes[i];
JobGenes[i] = JobGenes[r];
JobGenes[r] = temp;
// Mutate the delay time
r = SimpleRNG.Next(0, _modesLength);
double mutatedDelay = 0;
//mutatedDelay = SimpleRNG.GetExponential(_delayMean);
mutatedDelay = SimpleRNG.GetNormal(TimeGenes[r], 1.0);
//mutatedDelay = _rand.NextDouble() * _delayMean;
if (mutatedDelay < 0.0)
{
mutatedDelay = 0.0;
}
TimeGenes[r] = mutatedDelay;
}
}
//Mutate the Mode vector:
for (int i = 0; i < _modesLength; i++)
{
if (SimpleRNG.GetUniform() < _mutationRate)
{
ModeGenes[i] = SimpleRNG.Next(0, _numberOfModes);
}
}
}
public void Crossover(ref ScheduleGenome p2, ref ScheduleGenome o1)
{
int cutpoint = SimpleRNG.Next(0, _jobsLength);
//Debug.Write(Environment.NewLine + "Distance between " + p1 + " - " + p2 + ": " + population[p1].Distance(population[p2]));
for (int i = 0; i < _jobsLength; i++)
{
o1.JobGenes[i] = JobGenes[i];
}
Debug.Assert(o1.IsValid(), "Invalid creature before crossover");
List <int> remainder = new List <int>(p2.JobGenes);
for (int i = 0; i < cutpoint; i++)
{
remainder.Remove(JobGenes[i]);
}
for (int i = cutpoint; i < _jobsLength; i++)
{
o1.JobGenes[i] = remainder[i - cutpoint];
}
Debug.Assert(o1.IsValid(), "Invalid creature after crossover");
RealCrossover(ref p2, ref o1);
CombinationCrossover(ref p2, ref o1);
}
public void RealCrossover(ref ScheduleGenome p2, ref ScheduleGenome o1)
{
switch (realCrossover)
{
case RealCrossoverOp.MeanWithNoise:
// Mean-with-noise Crossover:
for (int i = 0; i < _modesLength; i++)
{
double mean = TimeGenes[i] + p2.TimeGenes[i];
mean = mean / 2.0;
o1.TimeGenes[i] = SimpleRNG.GetNormal(mean, 0.5);
if (o1.TimeGenes[i] < 0.0)
{
o1.TimeGenes[i] = 0.0;
}
}
break;
case RealCrossoverOp.Uniform:
// Uniform Crossover:
int cutpoint = SimpleRNG.Next(0, _modesLength + 1);
for (int i = 0; i < cutpoint; i++)
{
o1.TimeGenes[i] = TimeGenes[i];
}
for (int i = cutpoint; i < _modesLength; i++)
{
o1.TimeGenes[i] = p2.TimeGenes[i];
}
break;
}
}
public void RandomInit()
{
List <int> randarray = new List <int>();
for (int i = 0; i < _jobsLength; i++)
{
randarray.Add(i);
}
for (int i = 0; i < _jobsLength; i++)
{
int r = SimpleRNG.Next(0, _jobsLength - i);
JobGenes[i] = randarray[r];
randarray.RemoveAt(r);
}
for (int i = 0; i < _timesLength; i++)
{
if (SimpleRNG.GetUniform() < _delayRate)
{
TimeGenes[i] = SimpleRNG.GetExponential(_delayMean);
}
else
{
TimeGenes[i] = 0.0;
}
ModeGenes[i] = SimpleRNG.Next(0, _numberOfModes);
}
fitness = -1;
}
public void CombinationCrossover(ref ScheduleGenome p2, ref ScheduleGenome o1)
{
int cutpoint = SimpleRNG.Next(0, _modesLength);
for (int i = 0; i < cutpoint; i++)
{
o1.ModeGenes[i] = p2.ModeGenes[i];
}
for (int i = cutpoint; i < _modesLength; i++)
{
o1.ModeGenes[i] = ModeGenes[i];
}
}
private int SelectParent()
{
int p = 0;
switch (parentSelection)
{
case ParentSelectionOp.FitnessProportional:
double totalFitness = 0;
for (int i = 0; i < _popsize; i++)
{
totalFitness += population[i].fitness;
}
//double r = _rand.NextDouble() * totalFitness;
double r = SimpleRNG.GetUniform() * totalFitness;
double runningTotal = population[p].fitness;
while (runningTotal > r)
{
p++;
runningTotal += population[p].fitness;
}
break;
case ParentSelectionOp.Tournament:
int k = _popsize / 10;
//p = _rand.Next(_popsize);
p = (int)(SimpleRNG.GetUniform() * _popsize);
double bestfitness = population[p].fitness;
for (int i = 0; i < k; i++)
{
int px = SimpleRNG.Next(0, _popsize);
if (population[px].fitness > bestfitness)
{
bestfitness = population[px].fitness;
p = px;
}
}
break;
}
return(p);
}