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.weightsVect.Count; i++)
{
weight = weight + solution.weightsVect[i] + " ";
}
weight.Remove(weight.Length - 1, 1);
return(new List <string>()
{
theta, weight
});
}
public async void Reproduction(int[] token)
{
int maxNumOfTasks = 20;
GAEncoding child = new GAEncoding(pNumOfParams, dimension, lowbounds, highbounds);
int k = 0;
var rankOneSol = cePool.Where(x => x.Value[0] == cePool.Max(y => y.Value[0])).First();
tempPool.Add(Copy.DeepCopy(rankOneSol.Key), rankOneSol.Value);
List <Task> lTasks = new List <Task>();
if (cePool.Count < 100)
{
Console.WriteLine("RRRRRR");
}
while (tempPool.Count < popSize)
{
lTasks.Add(Task.Run(() =>
{
int[] selectedList = FitnessPropotionateSampling(pPanCombSize);
child = Copy.DeepCopy(cePool.ElementAt(selectedList[0]).Key);
if (GlobalVar.rnd.NextDouble() <= pmCrossOverRate)
{
if (cePool.Count < 100)
{
Console.WriteLine();
}
child = cePool.ElementAt(0).Key.IntermidinateRecomb(cePool, selectedList);
child = child.RealVectMutation();
GlobalVar.mutex_K.WaitOne();
if (!tempPool.ContainsKey(child))
{
tempPool.Add(child, new double[] { -1, -1 });
k = k + 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 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;
}
//Simple Mutation Assumes independence of each variable
internal GAEncoding RealVectMutation()
{
GAEncoding newSolution = Copy.DeepCopy(this);
UpdateID();
newSolution.FixInputs(_lowbounds, _highbounds);
//for (int i = 0; i < _numOfParams * _numOfParams; i++)
//{
// if (GlobalVar.rnd.NextDouble()
// <= 2.0 / (_numOfParams * _numOfParams))
// {
// double k_MutPrecision = 1;
// double u = GlobalVar.rnd.NextDouble();
// int s_Direction = GlobalVar.rnd.NextDouble() <= 0.5 ? -1 : 1;
// double a_StepSize1 = 10*u + s_Direction;
// double mean = weightsVect.Sum()/(_numOfParams*_numOfParams);
// weightsVect[i] = weightsVect[i] * a_StepSize1 + mean * (1 - a_StepSize1);
// if (weightsVect[i] <= 0)
// {
// weightsVect[i] = 0.0001;
// }
// }
//}
if (GlobalVar.rnd.NextDouble() <= 1.0 / 100)
{
for (int i = 0; i < _numOfParams * _numOfParams; i++)
{
double k_MutPrecision = 1;
double u = GlobalVar.rnd.NextDouble();
int s_Direction = GlobalVar.rnd.NextDouble() <= 0.5 ? -1 : 1;
double a_StepSize1 = 10 * u + s_Direction;
double mean = weightsVect.Sum() / (_numOfParams * _numOfParams);
weightsVect[i] = weightsVect[i] * a_StepSize1 + mean * (1 - a_StepSize1);
if (weightsVect[i] <= 0)
{
weightsVect[i] = 0.0001;
}
}
}
weightsVect = weightsVect.Normalize(1).Multiply(_numOfParams * _numOfParams);
double test = weightsVect.Sum();
if (test > _numOfParams * _numOfParams + 0.001 || test < _numOfParams * _numOfParams - 0.001)
{
Console.WriteLine();
}
return(newSolution);
}
private void GA_Initialization()
{
cePool = new CEPool();
tempPool = new CEPool();
ceDB = new CEPool();
cumulativeArray = new double[popSize];
Console.BackgroundColor = ConsoleColor.Yellow;
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 });
}
for (int i = 0; i < pNumOfParams * pNumOfParams; i++)
{
dt.Columns.Add(i.ToString(), Type.GetType("System.Double"));
}
}
// Recombination: create a new solution, call this function;
// Add the new solution to the population
internal GAEncoding IntermidinateRecomb(CEPool currentPool, int[] selectedList)
{
double d = 0.25;
GAEncoding newSolution = new GAEncoding(_numOfParams, _dimension, _lowbounds, _highbounds);
double alpha = GlobalVar.rnd.NextDouble();
alpha = (1 + d - -1 * d) * alpha + -1 * d;
var deltaW = currentPool.ElementAt(selectedList[1]).Key.weightsVect
- currentPool.ElementAt(selectedList[0]).Key.weightsVect;
newSolution.weightsVect =
deltaW.Multiply(alpha) + currentPool.ElementAt(selectedList[0]).Key.weightsVect;
double test = newSolution.weightsVect.Sum();
if (test > _numOfParams * _numOfParams + 0.001 || test < _numOfParams * _numOfParams - 0.001)
{
Console.WriteLine();
}
newSolution.FixInputs(_lowbounds, _highbounds);
newSolution.UpdateID();
return(newSolution);
}
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");
}
