public async void Reproduction(int[] token)
{
int maxNumOfTasks = 20;
int k = 0;
List <Task> lTasks = new List <Task>();
while (tempPool.Count < popSize)
{
lTasks.Add(Task.Run(() =>
{
GlobalVar.mutex_K.WaitOne();
int index = k;
k = k + 1;
GlobalVar.mutex_K.ReleaseMutex();
GAEncoding child = Copy.DeepCopy(cePool.ElementAt(index).Key);
int[] selectedList = DifferentialSelection(index);
child = child.DifferentialCrossover(cePool, selectedList);
child.FitnessCal(aMatrix);
if (child.entropy > cePool.ElementAt(index).Key.entropy
/*&& child.diversity > cePool.ElementAt(index).Key.diversity*/)
{
GlobalVar.mutex_K.WaitOne();
tempPool.Add(child, new double[] { child.entropy, -1 });
GlobalVar.mutex_K.ReleaseMutex();
}
else
{
GlobalVar.mutex_K.WaitOne();
tempPool.Add(cePool.ElementAt(index).Key,
new double[] { cePool.ElementAt(index).Key.entropy, -1 });
GlobalVar.mutex_K.ReleaseMutex();
}
}));
if (lTasks.Count == maxNumOfTasks)
{
for (int i = 0; i < lTasks.Count; i++)
{
await lTasks[i];
}
lTasks.Clear();
int remainTasks = cePool.Count - k;
if (remainTasks <= maxNumOfTasks)
{
maxNumOfTasks = remainTasks;
}
}
}
token[0] = 1;
}
public async void GA_FitnessEvaluation(int[] token)
{
List <Task> lTasks = new List <Task>();
// 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;
int id = k;
k = k + 1;
GlobalVar.mutex_K.ReleaseMutex();
//ga.CalEstFitness(testSetSize, numOfLabel, labelMatrix, lowbounds, highbounds);
//ga.TrueFitnessCal(numOfLabel, labelMatrix);
ga.FitnessCal(aMatrix);
//Console.WriteLine("{0} job finish",id);
}));
if (lTasks.Count == 20 || (tempPool.Count - k - 1 < 20)) //8,8
{
for (int i = 0; i < lTasks.Count; i++)
{
await lTasks[i];
}
lTasks.Clear();
}
}
token[0] = 1;
//Console.WriteLine("Fitness Evaluation Done");
}