public async void MOGA_FitnessEvaluation(int[] token)
{
List <Task> lTasks = new List <Task>();
int remainTasks = tempPool.Count;
// Test inputs Generation
// Fitness Evaluation
for (int k = 0; k < tempPool.Count;)
{
// Single Thread
//ga.CalEstFitness(testSetSize, numOfLabel, labelMatrix, lowbounds, highbounds);
lTasks.Add(Task.Run(() =>
{
GlobalVar.mutex_K.WaitOne();
//Console.WriteLine(k);
GAEncoding ga = tempPool.ElementAt(k).Key;
k = k + 1;
GlobalVar.mutex_K.ReleaseMutex();
//ga.CalEstFitness(testSetSize, numOfLabel, labelMatrix, lowbounds, highbounds);
ga.TrueFitnessCal(numOfLabel, labelMatrix);
// Console.WriteLine("{0}'th finished assement", k);
}));
if (lTasks.Count == 8 || (tempPool.Count - k - 1 < 8)) //8,8
{
for (int i = 0; i < lTasks.Count; i++)
{
await lTasks[i];
}
lTasks.Clear();
}
}
token[0] = 1;
//Console.WriteLine("Fitness Evaluation Done");
}
private void MOGA_Initialization()
{
cePool = new CEPool();
tempPool = new CEPool();
ceDB = new CEPool();
Console.BackgroundColor = ConsoleColor.DarkRed;
numOfNNLocalSearch = 1000; //(pNumOfParams + 1) * (pNumOfParams + 2) / 2 * dimension;
for (int i = 0; i < popSize; i++)
{
var newSolution = new GAEncoding(pNumOfParams, dimension, lowbounds, highbounds);
newSolution.FixInputs(lowbounds, highbounds);
tempPool.Add(newSolution, new double[] { -1, -1 });
}
}
internal GAEncoding PanmicticDiscreteRecomb(CEPool currentPool, int[] selectedList)
{
GAEncoding newSolution = new GAEncoding(_numOfParams, _dimension, _lowbounds, _highbounds);
for (int i = 0; i < _dimension; i++)
{
for (int j = 0; j < _numOfParams; j++)
{
int selectedIndex = selectedList[GlobalVar.rnd.Next(0, selectedList.Length - 1 + 1)];
newSolution.weights[j, i] = currentPool.ElementAt(selectedIndex).Key.weights[j, i];
}
}
newSolution.FixInputs(_lowbounds, _highbounds);
newSolution.UpdateID();
return(newSolution);
}
// Recombination: create a new solution, call this function;
// Add the new solution to the population
internal GAEncoding PanmicticAvgRecomb(CEPool currentPool, int[] selectedList)
{
// Select solution to combine
// Find Average
Matrix <double> avgWeight = Matrix.Build.Dense(_numOfParams, _dimension);
for (int i = 0; i < selectedList.Length; i++)
{
avgWeight += currentPool.ElementAt(selectedList[i]).Key.weights;
}
avgWeight = avgWeight / selectedList.Length;
// ... Check the sum of each row should be equal to a constant max
GAEncoding newSolution = new GAEncoding(_numOfParams, _dimension, _lowbounds, _highbounds);
newSolution.weights = avgWeight;
newSolution.FixInputs(_lowbounds, _highbounds);
return(newSolution);
}
public List <string> Print_Solution(GAEncoding solution)
{
string theta = null;
string weight = null;
for (int i = 0; i < solution.thetaDelta.RowCount; i++)
{
string tmp = "(";
Vector <double> vTemp = solution.thetaDelta.Row(i);
for (int j = 0; j < dimension; j++)
{
tmp = tmp + Math.Round(vTemp[j], 2) + " ";
}
tmp.Remove(tmp.Length - 1, 1);
tmp = tmp + ")";
theta = theta + tmp + " ";
}
theta.Remove(theta.Length - 1, 1);
for (int i = 0; i < solution.weights.RowCount; i++)
{
string tmp = "[";
Vector <double> vTemp = solution.weights.Row(i);
for (int j = 0; j < dimension; j++)
{
tmp = tmp + Math.Round(vTemp[j], 2) + " ";
}
tmp.Remove(tmp.Length - 1, 1);
tmp = tmp + "]";
weight = weight + tmp + " ";
}
weight.Remove(weight.Length - 1, 1);
return(new List <string>()
{
theta, weight
});
}
public void Reproduction()
{
GAEncoding child = new GAEncoding(pNumOfParams, dimension, lowbounds, highbounds);
int i = 0;
var rankOneSols = cePool.Where(x => x.Key.rank == 1).ToList();
foreach (var s in rankOneSols)
{
tempPool.Add(Copy.DeepCopy(s.Key), s.Value);
}
while (tempPool.Count < popSize)
{
int[] selectedList = FitnessPropotionateSampling(0, popSize - 1, pPanCombSize);
child = Copy.DeepCopy(cePool.ElementAt(selectedList[0]).Key);
if (GlobalVar.rnd.NextDouble() <= pmCrossOverRate)
{
if (GlobalVar.rnd.NextDouble() <= 0.8)
{
child = cePool.ElementAt(0).Key.PanmicticDiscreteRecomb(cePool, selectedList);
}
else
{
child = cePool.ElementAt(0).Key.PanmicticAvgRecomb(cePool, selectedList);
}
}
if (GlobalVar.rnd.NextDouble() <= pmMutationRate)
{
child.RealVectSimpleMutation();
}
if (child != null)
{
tempPool.Add(child, new double[] { -1, -1 });
i = i + 1;
}
}
}
