public Design Mutate(MathNet.Numerics.Distributions.IContinuousDistribution dist, double rate)
{
Design copy = (Design)this.DesignClone();
// loop over all the design variables
foreach (DesignVar var in copy.DesignVariables)
{
var.Mutate(rate, dist);
}
copy.CheckConstraints();
return(copy);
}
private double GetDistance(Design s1, Design s2)
{
double dist = 0;
for (int i = 0; i < s1.DesignVariables.Count; i++)
{
double var1 = s1.DesignVariables[i].Value;
double var2 = s2.DesignVariables[i].Value;
double sq = Math.Pow((var1 - var2), 2);
dist += sq;
}
dist = Math.Sqrt(dist);
return(dist);
}
public static List<Design> NewGeneration(Design myFirstDesign, List<Design> seeds, IContinuousDistribution dist, int popsize, double rate)
{
// generate new generation
List<Design> Designs = new List<Design>();
int count = 0;
while (count < popsize)
{
// generate random new design
Design randDesign = GenerateDesign( myFirstDesign, seeds, dist, rate);
count++;
Designs.Add(randDesign.DesignClone());
}
return Designs;
}
public static Design Mutate(Design d, IContinuousDistribution dist, double rate)
{
Design copy = d.DesignClone();
List<DesignVar> newdvars = new List<DesignVar>();
// loop over all the design variables
foreach (DesignVar var in copy.DesignVariables)
{
DesignVar varcopy = var.VarClone();
varcopy.Mutate(rate, dist);
varcopy.FixConstraint();
newdvars.Add(varcopy);
}
copy.DesignVariables = newdvars;
return copy;
}
public bool IsDiverse(List <IDesign> existing, IDesign candidate, double rate)
{
this.Best = existing;
this.Candidate = (Design)candidate;
this.MutationRate = rate;
double diff = this.GetDiffSize();
foreach (Design s in this.Best)
{
double dist = GetDistance(s, Candidate);
if (dist < diff)
{
return(false);
}
}
return(true);
}
