public override T Mutate <T>()
{
var newData = new List <double>(Vector);
double MutationChance = Manager.GetParameters().GetParameter("extra_MutationChance", 0.5);
dynamic Params = Manager.GetParameters().JsonParams.parameters;
int x = 0;
foreach (var P in Params)
{
if ((bool)P.enabled == true)
{
int ListSize = P.listsize != null ? (int)P.listsize : 1;
double MutateRange = (double)((double)P.rangeMax - (double)P.rangeMin) / (double)P.rangeAccuracy;
if (P.distinct == null || (bool)P.distinct == false)
{
for (int i = 0; i < ListSize; i++)
{
if (rand.Next(0, 100) <= MutationChance * 100)
{
/*double Shift = Math.Round(rand.NextDouble(-MutateRange, MutateRange), (int)Math.Log10((double)P.rangeAccuracy));
* newData[x] += Shift;
* if (newData[x] > (double)P.rangeMax)
* newData[x] = (double)P.rangeMax;
* if (newData[x] < (double)P.rangeMin)
* newData[x] = (double)P.rangeMin;*/
newData[x] = GetValInRange((double)P.rangeMin, (double)P.rangeMax, (int)P.rangeAccuracy);
}
x++;
}
}
else
{
var Allowed = new List <double>();
for (int i = (int)P.rangeMin; i <= (int)P.rangeMax; i++)
{
Allowed.Add(i);
}
foreach (var X in Vector)
{
Allowed.Remove(X);
}
for (int i = 0; i < ListSize; i++)
{
if (rand.Next(0, 100) <= MutationChance * 100)
{
double temp = newData[x];
newData[x] = Allowed[rand.Next(0, Allowed.Count)];
Allowed.Remove(newData[x]);
Allowed.Add(temp);
}
x++;
}
}
}
}
PopulationMember ret = new BalanceGA(Manager, newData, rand);
((BalanceGA)ret).CreatedBy = "Mutation";
return((T)ret);
}
public override PopulationMember Clone()
{
BalanceGA ret = new BalanceGA(Manager, new List <double>(Vector), rand);
return(ret);
}
public override T Crossover <T>(T b)
{
BalanceGA CWith = ((BalanceGA)(object)b);
var newData = new List <double>(Vector);
dynamic Params = Manager.GetParameters().JsonParams.parameters;
int x = 0;
int NonDistinctRangeStart = -1;
int NonDistinctRangeEnd = -1;
bool DistinctFlag = false;
int LastNonDistinctAccuracy = 1;
foreach (var P in Params)
{
if ((bool)P.enabled == true)
{
int ListSize = P.listsize != null ? (int)P.listsize : 1;
if (P.distinct == null || (bool)P.distinct == false)
{
if (NonDistinctRangeStart == -1)
{
NonDistinctRangeStart = x;
}
LastNonDistinctAccuracy = (int)P.rangeAccuracy;
NonDistinctRangeEnd = x + ListSize;
}
else
{
DistinctFlag = true;
var newSet = new HashSet <double>();
foreach (var X in Vector.Skip(x).Take(ListSize))
{
newSet.Add(X);
}
foreach (var X in CWith.Vector.Skip(x).Take(ListSize))
{
newSet.Add(X);
}
var vec = newSet.ToList();
vec.Shuffle();
var L = vec.Take(ListSize).ToList();
for (int i = 0; i < ListSize; i++)
{
newData[x + i] = L[i];
}
}
x += ListSize;
}
if (NonDistinctRangeStart != -1 && (DistinctFlag || NonDistinctRangeEnd == Vector.Count))
{
if (NonDistinctRangeEnd - NonDistinctRangeStart == 1)
{
newData[x] = Math.Round((Vector[NonDistinctRangeStart] + CWith.Vector[NonDistinctRangeStart]) / 2, (int)Math.Log10(LastNonDistinctAccuracy));
}
else
{
int p1 = rand.Next(NonDistinctRangeStart, NonDistinctRangeEnd - 1);
int p2 = rand.Next(p1 + 1, NonDistinctRangeEnd);
for (int i = p1; i < p2; i++)
{
newData[i] = CWith.Vector[i];
}
}
NonDistinctRangeStart = -1;
DistinctFlag = false;
}
}
PopulationMember ret = (T)Activator.CreateInstance(typeof(T), new object[] { Manager, newData, rand });
((BalanceGA)ret).CreatedBy = "Crossover";
return((T)ret);
}