//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);
}