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);
}
