public void beginCrossover()
{
_successfulCrossover = new List <TreePair>();
_toMutate = new List <GeneticTree>();
foreach (var item in _toCrossover)
{
if (Rng.Rnd.NextDouble() < _crossoverRate)
{
GeneticTree t1 = GeneticTree.deepCopy(item._first);
GeneticTree t2 = GeneticTree.deepCopy(item._second);
crossover(t1, t2);
_successfulCrossover.Add(new TreePair(GeneticTree.deepCopy(t1), GeneticTree.deepCopy(t2)));
_swapParents(t1, t2, item._first._id, item._second._id);
_toMutate.Add(t1);
_toMutate.Add(t2);
}
else
{
_toMutate.Add(item._first);
_toMutate.Add(item._second);
}
}
}
public void beginMutation()
{
_successfulMutation = new List <GeneticTree>();
foreach (var item in _toMutate)
{
item.setColor(item._nodesColor);
item.allNodesStats(item._root, 0, "", 0);
if (Rng.Rnd.NextDouble() < _mutationRate)
{
GeneticTree copy = GeneticTree.deepCopy(item);
mutateOpportunistic(copy);
_successfulMutation.Add(copy);
}
else
{
_newPopulation.Add(item);
}
}
}
public void beginSelection()
{
_newPopulation = new List <GeneticTree>();
_toCrossover = new List <TreePair>();
int elitismPassNumber = (int)(_populationSize * _elitismPercentage);
if (elitismPassNumber % 2 != 0)
{
elitismPassNumber++;
}
for (int i = 0; i < elitismPassNumber; i++)
{
_newPopulation.Add(GeneticTree.deepCopy(_population[i]));
}
switch (_selectionType)
{
case GpSelectionType.RANK:
int[] weights = Enumerable.Range(1, (int)_populationSize).Reverse().ToArray();
int[] weightsRev = weights.Reverse().ToArray();
uint[] weightSum = new uint[_populationSize];
weightSum[0] = _populationSize;
for (uint i = 1; i < weightSum.Length; i++)
{
weightSum[i] = _populationSize - i + weightSum[i - 1];
}
int sum = ((1 + (int)_populationSize)) * (int)_populationSize / 2;
for (int i = 0; i < _populationSize - elitismPassNumber; i += 2)
{
int centil = Rng.Rnd.Next(0, sum);
int resIndex = weightsRev.First(x => weightSum[x - 1] > centil) - 1;
GeneticTree first = _population[resIndex];
centil = Rng.Rnd.Next(0, sum);
resIndex = weightsRev.First(x => weightSum[x - 1] > centil) - 1;
GeneticTree second = _population[resIndex];
_toCrossover.Add(new TreePair(GeneticTree.deepCopy(first), GeneticTree.deepCopy(second)));
}
break;
case GpSelectionType.ROULETTE:
weights = Enumerable.Range(0, (int)_populationSize).ToArray();
weightsRev = weights.Reverse().ToArray();
float[] wSum = new float[(int)_populationSize];
wSum[0] = _fitnesses[0];
for (int i = 1; i < wSum.Length; i++)
{
wSum[i] = _fitnesses[i] + wSum[i - 1];
}
float sumF = _fitnesses.Sum();
for (int i = 0; i < _populationSize - elitismPassNumber; i += 2)
{
double centil = Rng.Rnd.NextDouble() * sumF;
int resIndex = weights.First(x => wSum[x] > centil);
GeneticTree first = _population[resIndex];
centil = Rng.Rnd.NextDouble() * sumF;
resIndex = weights.First(x => wSum[x] > centil);
GeneticTree second = _population[resIndex];
_toCrossover.Add(new TreePair(GeneticTree.deepCopy(first), GeneticTree.deepCopy(second)));
}
break;
case GpSelectionType.TOURNAMENT:
weights = Enumerable.Range(0, (int)_populationSize).ToArray();
var shuffled = weights.OrderBy(x => Guid.NewGuid());
for (int i = 0; i < _populationSize - elitismPassNumber; i += 2)
{
var slice = shuffled.Take((int)_tournamentSelectionSize);
var firstRandom = _population[slice.OrderByDescending(x => _fitnesses[x]).First()];
shuffled = weights.OrderBy(x => Guid.NewGuid());
slice = shuffled.Take((int)_tournamentSelectionSize);
var secondRandom = _population[slice.OrderByDescending(x => _fitnesses[x]).First()];
shuffled = weights.OrderBy(x => Guid.NewGuid());
_toCrossover.Add(new TreePair(GeneticTree.deepCopy(firstRandom), GeneticTree.deepCopy(secondRandom)));
}
break;
}
}