/// <summary>
/// Returns the Manhattan difference between two Fitnesses in Objective space.
/// </summary>
public double ManhattanObjectiveDistance(MultiObjectiveFitness other)
{
double s = 0;
for (var i = 0; i < Objectives.Length; i++)
{
s += Math.Abs(Objectives[i] - other.Objectives[i]);
}
return(s);
}
/// <summary>
/// Returns the sum of the squared difference between two Fitnesses in Objective space.
/// </summary>
/// <param name="other"></param>
/// <returns></returns>
public double SumSquaredObjectiveDistance(MultiObjectiveFitness other)
{
double s = 0;
for (var i = 0; i < Objectives.Length; i++)
{
double a = Objectives[i] - other.Objectives[i];
s += a * a;
}
return(s);
}
/// <summary>
/// Logs the best individual of the run.
/// </summary>
public override void FinalStatistics(IEvolutionState state, int result)
{
BypassFinalStatistics(state, result); // just call base.base.finalStatistics(...)
if (DoFinal)
{
state.Output.PrintLn("\n\n\n PARETO FRONTS", StatisticsLog);
}
for (var s = 0; s < state.Population.Subpops.Count; s++)
{
if (DoFinal)
{
state.Output.PrintLn("\n\nPareto Front of Subpopulation " + s, StatisticsLog);
}
// build front
var front = MultiObjectiveFitness.PartitionIntoParetoFront(state.Population.Subpops[s].Individuals, null, null);
// sort by objective[0]
var sortedFront = front.ToArray();
QuickSort.QSort(sortedFront, new MultiObjectiveFitnessComparator());
// print out front to statistics log
if (DoFinal)
{
foreach (var ind in sortedFront)
{
ind.PrintIndividualForHumans(state, StatisticsLog);
}
}
// write short version of front out to disk
if (FrontLog >= 0)
{
if (state.Population.Subpops.Count > 1)
{
state.Output.PrintLn("Subpopulation " + s, FrontLog);
}
foreach (var ind in sortedFront)
{
var mof = (MultiObjectiveFitness)ind.Fitness;
var objectives = mof.GetObjectives();
var line = objectives.Aggregate("", (current, t1) => current + (t1 + " "));
state.Output.PrintLn(line, FrontLog);
}
}
}
}
public void SetToMeanOf(IEvolutionState state, Fitness[] fitnesses)
{
// basically we compute the centroid of the fitnesses
double sum = 0.0;
for (int i = 0; i < Objectives.Length; i++)
{
for (int k = 0; k < fitnesses.Length; k++)
{
MultiObjectiveFitness f = (MultiObjectiveFitness)fitnesses[k];
sum += f.Objectives[i];
}
Objectives[i] = (double)(sum / fitnesses.Length);
}
}
/// <summary>
/// Returns true if I'm better than _fitness. The rule I'm using is this: if
/// I am better in one or more criteria, and we are equal in the others, then
/// betterThan is true, else it is false.
/// </summary>
/// <param name="other"></param>
/// <returns></returns>
public bool ParetoDominates(MultiObjectiveFitness other)
{
var abeatsb = false;
if (Objectives.Length != other.Objectives.Length)
{
throw new ApplicationException(
"Attempt made to compare two multiobjective fitnesses; but they have different numbers of objectives.");
}
for (int x = 0; x < Objectives.Length; x++)
{
if (Maximize[x] != other.Maximize[x]) // uh oh
{
throw new InvalidOperationException(
"Attempt made to compare two multiobjective fitnesses; but for objective #" + x +
", one expects higher values to be better and the other expectes lower values to be better.");
}
if (Maximize[x])
{
if (Objectives[x] > other.Objectives[x])
{
abeatsb = true;
}
else if (Objectives[x] < other.Objectives[x])
{
return(false);
}
}
else
{
if (Objectives[x] < other.Objectives[x])
{
abeatsb = true;
}
else if (Objectives[x] > other.Objectives[x])
{
return(false);
}
}
}
return(abeatsb);
}
