Changeset 13620 for branches

branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/Comparators/Crowding.cs

-                      r13562
+                      r13620
 using System;
 using System.Collections.Generic;
+using System.Linq;
+using HeuristicLab.Data;
 using HeuristicLab.Encodings.RealVectorEncoding;
+using HeuristicLab.Problems.MultiObjectiveTestFunctions.Comparators;
 namespace HeuristicLab.Problems.MultiObjectiveTestFunctions {
   /// <summary>
   /// Crowding distance d(x,A) is usually defined between a point x and a set of points  A
+  /// Crowding distance d(x,A) is usually defined between a point x and a set of points A
   /// d(x,A) is then a weighted sum over all dimensions where for each dimension the next larger and the next smaller Point to x are subtracted
   /// I extended the concept and defined the Crowding distance of a front A as the mean of the crowding distances of every point x in A
   /// C(A) = mean(x,A) where x in A
+  /// C(A) = mean(d(x,A)) where x in A  and d(x,A) is not infinite
   /// </summary>
   public class Crowding : IMultiObjectiveDistance {
+    private double[][] bounds;
+    public Crowding(double[][] bounds) {
+    private double[,] bounds;
+    public Crowding(double[,] bounds) {
       this.bounds = bounds;
+    }
+    public static double GetDistance(RealVector[] front, RealVector[] optimalFront, double[][] bounds) {
+    public static double GetDistance(IEnumerable<double[]> front, IEnumerable<double[]> optimalFront, double[,] bounds) {
       return new Crowding(bounds).Compare(front, optimalFront);
+    }
+    public static double GetCrowding(RealVector[] front, double[][] bounds) {
+    public static double GetCrowding(IEnumerable<double[]> front, double[,] bounds) {
       return new Crowding(bounds).Get(front);
+    }
+    public double Get(RealVector[] front) {
+    public double Get(IEnumerable<double[]> front) {
       double sum = 0;
+      foreach(RealVector point in front) {
+        sum += CalcCrowding(point, front);
+      int c = 0;
+      foreach (double[] point in front) {
+        double d = CalcCrowding(point, front);
+        if (!Double.IsInfinity(d)) {
+          sum += d;
+          c++;
+        }
+      }
       return sum / front.Length;
+      return c==0?Double.PositiveInfinity:sum / c;
+    }
     public double Compare(RealVector[] front, RealVector[] optimalFront) {
+    public double Compare(IEnumerable<double[]> front, IEnumerable<double[]> optimalFront) {
       return Get(optimalFront) - Get(front);
+    }
     private double CalcCrowding(RealVector point, RealVector[] list) {
+    private double CalcCrowding(double[] point, IEnumerable<double[]> list) {
       double sum = 0;
       for (int i = 0; i < point.Length; i++) {
 …
+    }
+    private double CalcCrowding(RealVector point, RealVector[] list, int dim) {
+      double fmax = bounds[dim % bounds.Length][1];
+      double fmin = bounds[dim % bounds.Length][0];
+      Array.Sort<RealVector>(list, new DimensionComparer(dim, false));
+      if (list[0][dim] == point[0] || list[list.Length - 1][dim] == point[0]) return Double.PositiveInfinity;
+      int pos = binarySearch(point[dim], list, dim);
+      return (list[pos + 1][dim] - list[pos - 1][dim] )/ (fmax - fmin);
+    }
+    private int binarySearch(double x, RealVector[] list, int dim) {
+      int low = 0;
+      int high = list.Length - 1;
+      while (high >= low) {
+        int middle = (low + high) / 2;
+        if (list[middle][dim] == x) return middle;
+        if (list[middle][dim] < x)  low = middle + 1;
+        if (list[middle][dim] > x)  high = middle - 1;
+      }
+      return -1; //This should never happen
+    }
+    private class DimensionComparer : IComparer<RealVector> {
+      private int dim;
+      private int descending;
+      public DimensionComparer(int dimension, bool descending) {
+        this.dim = dimension;
+        this.descending = descending ? -1 : 1;
+      }
+      #region IComparer<DoubleArray> Members
+      public int Compare(RealVector x, RealVector y) {
+        if (x[dim] < y[dim]) return -descending;
+        else if (x[dim] > y[dim]) return descending;
+        else return 0;
+      }
+      #endregion
+    private double CalcCrowding(double[] point, IEnumerable<double[]> list, int dim) {
+      double fmax = bounds[dim % bounds.GetLength(0), 1];
+      double fmin = bounds[dim % bounds.GetLength(0), 0];
+      double[][] arr = list.ToArray();  //TODO Shady
+      Array.Sort<double[]>(arr, Utilities.getDimensionComparer(dim, false));
+      if (arr[0][dim] == point[0] || arr[arr.Length - 1][dim] == point[0]) return Double.PositiveInfinity;
+      int pos = Utilities.binarySearch(point[dim], arr, dim);
+      if (pos == 0 || pos == list.Count()-1) return Double.PositiveInfinity;
+      return (arr[pos + 1][dim] - arr[pos - 1][dim]) / (fmax - fmin);
+    }

branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/Comparators/GenerationalDistance.cs

-                      r13562
+                      r13620
 using System;
+using System.Collections.Generic;
 using HeuristicLab.Encodings.RealVectorEncoding;
+using HeuristicLab.Problems.MultiObjectiveTestFunctions.Comparators;
 namespace HeuristicLab.Problems.MultiObjectiveTestFunctions {
 …
+    }
+    public static double GetDistance(RealVector[] front, RealVector[] optimalFront, double p) {
+    public static double GetDistance(IEnumerable<double[]> front, IEnumerable<double[]> optimalFront, double p) {
      return new GenerationalDistance(p).Compare(front,optimalFront);
+    }
+    public double Compare(RealVector[] front, RealVector[] optimalFront) {
+     //TODO build a kd-tree, sort the array, do someting intelligent here
+     double sum = 0;
+      if (front.Length == 0 || optimalFront.Length == 0) throw new Exception("Both Fronts need to contain at least one point");
+     foreach(RealVector r in front) {
+        sum += minDistance(r, optimalFront);
+    public double Compare(IEnumerable<double[]> front, IEnumerable<double[]> optimalFront) {
+      //TODO build a kd-tree, sort the array, do someting intelligent here
+      if (front == null || optimalFront == null) throw new ArgumentException("Fronts must not be null");
+      double sum = 0;
+      int c = 0;
+     foreach(double[] r in front) {
+        sum += Utilities.minDistance(r, optimalFront,true);
+        c++;
+      }
+      return Math.Pow(sum, p) /front.Length;
+      if (c == 0) throw new ArgumentException("Fronts must not be empty");
+      return Math.Pow(sum, p) /c;
+    }
+    private double minDistance(RealVector point, RealVector[] list) {
+      //TODO inefficient
+      double min = Double.MaxValue;
+      foreach (RealVector r in list) {
+        if (r == point) continue;
+        double d = 0;
+        for (int i = 0; i < r.Length; i++) {
+          d += (point[i] - r[i]) * (point[i] - r[i]);
+        }
+        min = Math.Min(d, min);
+      }
+      return Math.Sqrt(min);
+    }
+  }

branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/Comparators/HyperVolume.cs

-                      r13562
+                      r13620
 using System;
 using System.Collections.Generic;
+using HeuristicLab.Encodings.RealVectorEncoding;
+using System.Linq;
+using HeuristicLab.Problems.MultiObjectiveTestFunctions.Comparators;
 namespace HeuristicLab.Problems.MultiObjectiveTestFunctions {
 …
   public class Hypervolume : IMultiObjectiveDistance {
     private RealVector reference;
+    private double[] reference;
     private bool[] maximization;
     public Hypervolume(RealVector reference, bool[] maximization) {
+    public Hypervolume(double[] reference, bool[] maximization) {
       if (reference.Length != 2) throw new Exception("Only 2-dimensional cases are supported yet");
       this.reference = reference;
 …
+    }
     public double Compare(RealVector[] front, RealVector[] optimalFront) {
+    public double Compare(IEnumerable<double[]> front, IEnumerable<double[]> optimalFront) {
       return GetHypervolume(optimalFront) - GetHypervolume(front);
+    }
+    public double GetHypervolume(RealVector[] front) {
+    public double GetHypervolume(IEnumerable<double[]> front) {
+      if (front == null) throw new ArgumentException("Fronts must not be null");
       //TODO what to do if set contains dominated points
+      front = (RealVector[])front.Clone(); //TODO this seems shady
+      Array.Sort<RealVector>(front, new DimensionComparer(0, maximization[0]));
+      RealVector last = front[front.Length - 1];
+      double[][] set = front.ToArray();   //Still no Good
+      if (set.Length == 0) throw new ArgumentException("Fronts must not be empty");
+      Array.Sort<double[]>(set, Utilities.getDimensionComparer(0, maximization[0]));
+      double[] last = set[set.Length - 1];
       CheckConsistency(last, 0);
       CheckConsistency(last, 1);
       double sum = 0;
       for (int i = 0; i < front.Length - 1; i++) {
         CheckConsistency(front[i], 1);
         sum += Math.Abs((front[i][0] - front[i + 1][0])) * Math.Abs((front[i][1] - reference[1]));
+      for (int i = 0; i < set.Length - 1; i++) {
+        CheckConsistency(set[i], 1);
+        sum += Math.Abs((set[i][0] - set[i + 1][0])) * Math.Abs((set[i][1] - reference[1]));
+      }
 …
+    }
     public static double GetHypervolume(RealVector[] front, RealVector reference, bool[] maximization){
+    public static double GetHypervolume(IEnumerable<double[]> front, double[] reference, bool[] maximization){
       Hypervolume comp = new Hypervolume(reference, maximization);
       return comp.GetHypervolume(front);
+    }
     public static double GetDistance(RealVector[] front, RealVector[] optimalFront, RealVector reference, bool[] maximization) {
+    public static double GetDistance(IEnumerable<double[]> front, IEnumerable<double[]> optimalFront, double[] reference, bool[] maximization) {
       return GetHypervolume(optimalFront, reference, maximization) - GetHypervolume(front, reference, maximization);
+    }
     private void CheckConsistency(RealVector point, int dim) {
       if (!maximization[dim] && point[dim] > reference[dim]) throw new Exception("Reference Point must be dominated by all points of the front");
       if (maximization[dim] && point[dim] < reference[dim]) throw new Exception("Reference Point must be dominated by all points of the front");
       if (point.Length != 2) throw new Exception("Only 2-dimensional cases are supported yet");
+    private void CheckConsistency(double[] point, int dim) {
+      if (!maximization[dim] && point[dim] > reference[dim]) throw new ArgumentException("Reference Point must be dominated by all points of the front");
+      if (maximization[dim] && point[dim] < reference[dim]) throw new ArgumentException("Reference Point must be dominated by all points of the front");
+      if (point.Length != 2) throw new ArgumentException("Only 2-dimensional cases are supported yet");
+    }
-    private class DimensionComparer : IComparer<RealVector> {
-      private int dim;
-      private int descending;
-      public DimensionComparer(int dimension, bool descending) {
-        this.dim = dimension;
-        this.descending = descending ? -1 : 1;
+      }
-      #region IComparer<DoubleArray> Members
-      public int Compare(RealVector x, RealVector y) {
-        if (x[dim] < y[dim]) return -descending;
-        else if (x[dim] > y[dim]) return descending;
-        else return 0;
+      }
-      #endregion
+    }
+  }
+}

branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/Comparators/HyperVolumeFast.cs

-                      r13562
+                      r13620
 using System;
 using System.Collections.Generic;
+using HeuristicLab.Encodings.RealVectorEncoding;
+using HeuristicLab.Common;
+using HeuristicLab.Problems.MultiObjectiveTestFunctions.Comparators;
 namespace HeuristicLab.Problems.MultiObjectiveTestFunctions {
 …
   public class FastHypervolume {
     private RealVector reference;
+    private double[] reference;
     private bool[] maximization;
     public FastHypervolume(RealVector reference, bool[] maximization) {
       if (reference.Length != 2) throw new Exception("Only 2-dimensional cases are supported yet");
+    public FastHypervolume(double[] reference, bool[] maximization) {
+      if (reference.Length != 2) throw new NotSupportedException("Only 2-dimensional cases are supported yet");
       this.reference = reference;
       this.maximization = maximization;
+    }
     public double Compare(RealVector[] front, RealVector[] optimalFront, double[][] bounds) {
+    public double Compare(IEnumerable<double[]> front, IEnumerable<double[]> optimalFront, double[,] bounds) {
       return GetHypervolume(optimalFront, bounds) - GetHypervolume(front, bounds);
 …
     /// <param name="bounds">also called region</param>
     /// <returns></returns>
     public double GetHypervolume(RealVector[] front, double[][] bounds) {
+    public double GetHypervolume(IEnumerable<double[]> front, double[,] bounds) {
       //TODO what to do if set contains dominated points
+      var list = new List<RealVector>();
+      if (front == null) throw new ArgumentException("Fronts must not be null");
+      var list = new List<double[]>();
       list.AddRange(front);
       list.Sort(new DimensionComparer(reference.Length - 1, maximization[reference.Length - 1]));
+      list.Sort(Utilities.getDimensionComparer(reference.Length - 1, maximization[reference.Length - 1]));
       var results = new ExPrivates(this);
       GetHV(DeepClone(bounds), list, 1, reference[reference.Length - 1], results);
+      GetHV(SpanRegion(), list, 0, reference[reference.Length - 1], results);
       return results.Volume;
+    }
+    private double[][] SpanRegion(double[,] bounds, int length) {
+      if (bounds.GetLength(1) != 2) throw new ArgumentException();
+      double[][] copy = new double[length][];
+      for (int i = 0; i < copy.Length; i++) {
+        copy[i] = new double[2];
+        for (int j = 0; j < 2; j++) {
+          copy[i][j] = bounds[i%bounds.GetLength(0),j];
+        }
+      }
+      return copy;
+    }
+    private double[][] SpanRegion() {
+      double[][] copy = new double[reference.Length][];
+      for (int i = 0; i < copy.Length; i++) {
+        copy[i] = new double[2];
+        copy[i][0] = 0;
+        copy[i][1] = reference[i];
+      }
+      return copy;
+    }
     private double[][] DeepClone(double[][] array) {
 …
       for (int i = 0; i < copy.Length; i++) {
         copy[i] = new double[array[i].Length];
         for (int j = 0; i < array[i].Length; j++) {
+        for (int j = 0; j < array[i].Length; j++) {
           copy[i][j] = array[i][j];
+        }
 …
       public int D {
         get { return D; }
+        get { return d; }
+      }
       public ExPrivates(FastHypervolume hv) {
 …
+    }
     private void GetHV(double[][] region, List<RealVector> points, int split, double cover, ExPrivates results) {
+    private void GetHV(double[][] region, List<double[]> points, int split, double cover, ExPrivates results) {
       double coverNew = cover;
       int coverIndex = 1;
+      int coverIndex = 0;
       bool allPiles = true;
       int bound = -1;
+      double bound = -1;
       /* is the region completely covered? */
 …
         if (covers(points[coverIndex], region, results)) {
           coverNew = points[coverIndex][results.D];
+          results.Volume += getMeasure(region) * (cover - coverNew);
+        } else { }
+        coverIndex++;
+      }
+      if (coverIndex == 1) return;
+      for (int i = 1; i < coverIndex; i++) { if (checkPile(points[i], region) == -1) allPiles = false; }
+          results.Volume += GetMeasure(region,results) * (cover - coverNew);
+        } else coverIndex++;
+      }
+      if (coverIndex == 0) return;
+      for (int i = 0; i < coverIndex; i++) { if (checkPile(points[i], region, results) == -1) allPiles = false; }
       if (allPiles) {
         /* calculate volume by sweeping along dimension d */
         var trellis = new double[reference.Length];
         int i = 1;
         for (int j = 1; j < results.D; j++) trellis[j] = reference[j];
+        int i = 0;
+        for (int j = 0; j < results.D-1; j++) trellis[j] = reference[j];
         double next;//bernhard
         do {
           double current = points[i][results.D];
+          double current = points[i][results.D-1];
           do {
             int pile = getPile(points[i], region);
+            int pile = checkPile(points[i], region,results);
             if (points[i][pile] < trellis[pile]) trellis[pile] = points[i][pile];
             i++;
             if (i < coverIndex - 1) next = points[i][results.D]; else next = coverNew;
+            if (i < coverIndex - 1) next = points[i][results.D-1]; else next = coverNew;
           } while (current == next);
           results.Volume += measure(trellis, region) * (next - current);
+          results.Volume += measure(trellis, region, results) * (next - current);
         } while (next != coverNew);
 …
         do {
           var intersect = new List<Double>(); var nonIntersect = new List<Double>();
           for (int i = 1; i < coverIndex; i++) {
+          for (int i = 0; i < coverIndex; i++) {
             var intersection = intersects(points[i], region, split);
             if (intersection == 1) intersect.Add(points[i][split]);
             if (intersection == 0) nonIntersect.Add(points[i][split]);
+          }
           if (intersect.Count != 0) bound = median(intersect);
           else if (nonIntersect.Count > Math.Sqrt(n)) bound = median(nonIntersect);
+          if (intersect.Count != 0) bound = intersect.Median();
+          else if (nonIntersect.Count > Math.Sqrt(points.Count)) bound = nonIntersect.Median();
           else split++;
         } while (bound == -1);
 …
       var regionC = DeepClone(region);
       regionC[split][1] = bound;
       var pointsC = new List<RealVector>();
       for (int i = 1; i < coverIndex; i++) {
         if (partCovers(points[i], regionC, results)) move(points[i], pointsC);
+      var pointsC = new List<double[]>();
+      for (int i = 0; i < coverIndex; i++) {
+        if (partCovers(points[i], regionC, results)) move(points,i, pointsC);
+      }
       if (pointsC.Count != 0) GetHV(regionC, pointsC, split, coverNew, results);
 …
       regionC = region;
       regionC[split][0] = bound;
       pointsC = new List<RealVector>();
+      pointsC = new List<double[]>();
       for (int i = 1; i < coverIndex; i++) {
         if (partCovers(points[i], regionC, results)) move(points[i], pointsC);
+        if (partCovers(points[i], regionC, results)) move(points,i, pointsC);
+      }
       if (pointsC.Count != 0) GetHV(regionC, pointsC, split, coverNew, results);
 …
+    }
+    private int checkPile(RealVector point, double[][] region, ExPrivates results) {
+    private void reinsert(List<double[]> pointsC, List<double[]> points) {
+      points.AddRange(pointsC);
+      pointsC.Clear();
+    }
+    private double GetMeasure(double[][] region,ExPrivates result) {
+      double volume = 1.0;
+      // for ( std::size_t i = 0; i < regionLow.size(); i++ ) {
+      for (int i = 1; i < result.D; i++) {
+      volume *= (region[1][i] - region[0][i]);
+      }
+      return (volume);
+    }
+    private void move(List<double[]> points, int i, List<double[]> pointsC) {
+      double[] v = points[i];
+      points.Remove(v);
+      pointsC.Add(v);
+    }
+    private double measure(double[] trellis, double[][] region, ExPrivates results) {
+      double volume=0;
+      bool[] indicator = new bool[results.D];
+      for (int i = 0; i < results.D-1; i++) indicator[i] = true;
+      int numberSummands = integerValue(indicator);
+      for (int i = 0; i <= numberSummands; i++) {
+        indicator = binaryValue(i);
+        int oneCounter = 0;
+        double summand = 0;
+        for (int j = 1; j < results.D; j++) {
+          if (indicator[i] == true) {
+            summand += region[1][j] - trellis[j];
+            oneCounter++;
+          } else {
+            summand += region[1][j] - region[0][j];
+          }
+        }
+        if(oneCounter%2 == 0) {
+          volume -= summand;
+        } else {
+          volume += summand;
+        }
+      }
+      return volume;
+    }
+    private int integerValue(bool[] binary) {
+      int sum=0;
+      foreach (bool b in binary) {
+        sum = (sum << 1) + (b ? 1 : 0);
+      }
+      return sum;
+    }
+    private bool[] binaryValue(int integer) {
+      bool[] res = new bool[32];
+      int i = 0;
+      while (integer != 0) {
+        res[i++]= (integer & 1)==1;
+        integer >>= 1;
+      }
+      return res;
+    }
+    private int checkPile(double[] point, double[][] region, ExPrivates results) {
       int pile = -1;
       for (int j = 1; j < results.D; j++) {
+      for (int j = 0; j < results.D-1; j++) {
         if (point[j] > region[j][0]) {
           if (pile != -1) return -1;
 …
+    }
     private int intersects(RealVector point, double[][] region, int split) {
+    private int intersects(double[] point, double[][] region, int split) {
       if (region[split][0] >= point[split]) return -1;
       for (int j = 1; j < split; j++) {
 …
+    }
     private bool covers(RealVector point, double[][] region, ExPrivates results) {
+    private bool covers(double[] point, double[][] region, ExPrivates results) {
       for (int j = 1; j < results.D; j++) {
         if (point[j] > region[j][0]) return false;
 …
+    }
     private bool partCovers(RealVector point, double[][] region, ExPrivates results) {
+    private bool partCovers(double[] point, double[][] region, ExPrivates results) {
       for (int j = 1; j < results.D; j++) {
         if (point[j] >= region[j][1]) return false;
 …
+    }
     public static double GetHypervolume(RealVector[] front, RealVector reference, bool[] maximization) {
       Hypervolume comp = new Hypervolume(reference, maximization);
       return comp.GetHypervolume(front);
+    }
     public static double GetDistance(RealVector[] front, RealVector[] optimalFront, RealVector reference, bool[] maximization) {
       return GetHypervolume(optimalFront, reference, maximization) - GetHypervolume(front, reference, maximization);
+    }
     private void CheckConsistency(RealVector point, int dim) {
+    public static double GetHypervolume(IEnumerable<double[]> front, double[] reference, bool[] maximization, double[,] bounds) {
+      FastHypervolume comp = new FastHypervolume(reference, maximization);
+      return comp.GetHypervolume(front,bounds);
+    }
+    public static double GetDistance(IEnumerable<double[]> front, IEnumerable<double[]> optimalFront, double[] reference, bool[] maximization, double[,] bounds) {
+      return GetHypervolume(optimalFront, reference, maximization, bounds) - GetHypervolume(front, reference, maximization,bounds);
+    }
+    private void CheckConsistency(double[] point, int dim) {
       if (!maximization[dim] && point[dim] > reference[dim]) throw new Exception("Reference Point must be dominated by all points of the front");
       if (maximization[dim] && point[dim] < reference[dim]) throw new Exception("Reference Point must be dominated by all points of the front");
+    }
+    private class DimensionComparer : IComparer<RealVector> {
+      private int dim;
+      private int descending;
+      public DimensionComparer(int dimension, bool descending) {
+        this.dim = dimension;
+        this.descending = descending ? -1 : 1;
+      }
+      #region IComparer<DoubleArray> Members
+      public int Compare(RealVector x, RealVector y) {
+        if (x[dim] < y[dim]) return -descending;
+        else if (x[dim] > y[dim]) return descending;
+        else return 0;
+      }
+      #endregion
+    }
+  }

branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/Comparators/InvertedGenerationalDistance.cs

-                      r13562
+                      r13620
 using System;
+using System.Collections.Generic;
 using HeuristicLab.Encodings.RealVectorEncoding;
+using HeuristicLab.Problems.MultiObjectiveTestFunctions.Comparators;
 namespace HeuristicLab.Problems.MultiObjectiveTestFunctions {
 …
     public InvertedGenerationalDistance(double p) {
+      if (p <= 0) throw new ArgumentOutOfRangeException("weighting factor p has to be greater than 0");
       this.p = 1 / p;
+    }
     /// <summary>
 …
     /// <param name="p"></param>
     /// <returns></returns>
     public static double GetDistance(RealVector[] front, RealVector[] optimalFront, double p) {
+    public static double GetDistance(IEnumerable<double[]> front, IEnumerable<double[]> optimalFront, double p) {
       return new InvertedGenerationalDistance(p).Compare(front, optimalFront);
+    }
+    public double Compare(RealVector[] front, RealVector[] optimalFront) {
+      //TODO build a kd-tree, sort the array, do someting intelligent here
+      double sum = 0;
+      if (front.Length == 0 || optimalFront.Length == 0) throw new Exception("Both Fronts need to contain at least one point");
+      foreach (RealVector r in optimalFront) {
+        sum += minDistance(r, front);
+      }
+      return Math.Pow(sum, p) / optimalFront.Length;
+    public double Compare(IEnumerable<double[]> front, IEnumerable<double[]> optimalFront) {
+      return new GenerationalDistance(p).Compare(optimalFront, front);
+    }
-    private double minDistance(RealVector point, RealVector[] list) {
-      //TODO inefficient
-      double min = Double.MaxValue;
-      foreach (RealVector r in list) {
-        if (r == point) continue;
-        double d = 0;
-        for (int i = 0; i < r.Length; i++) {
-          d += (point[i] - r[i]) * (point[i] - r[i]);
+        }
-        min = Math.Min(d, min);
+      }
-      return Math.Sqrt(min);
+    }
+  }
+}

branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/Comparators/Spacing.cs

-                      r13562
+                      r13620
 using System;
+using System.Collections.Generic;
+using HeuristicLab.Common;
 using HeuristicLab.Encodings.RealVectorEncoding;
+using HeuristicLab.Problems.MultiObjectiveTestFunctions.Comparators;
 namespace HeuristicLab.Problems.MultiObjectiveTestFunctions {
 …
     public static double GetSpacing(RealVector[] front) {
+    public static double GetSpacing(IEnumerable<double[]> front) {
       return new Spacing().Get(front);
+    }
     public static double GetDistance(RealVector[] front, RealVector[] optimalFront) {
+    public static double GetDistance(IEnumerable<double[]> front, IEnumerable<double[]> optimalFront) {
       return new Spacing().Get(front);
+    }
     public double Compare(RealVector[] front, RealVector[] optimalFront) {
+    public double Compare(IEnumerable<double[]> front, IEnumerable<double[]> optimalFront) {
       return GetSpacing(front) - GetSpacing(optimalFront);
+    }
     public double Get(RealVector[] front) {
+    public double Get(IEnumerable<double[]> front) {
       //TODO build a kd-tree, sort the array, do someting intelligent here
+      double sum = 0;
+      if (front.Length == 0) throw new Exception("Front does not contain any points");
+      double[] d = new double[front.Length];
+      int i = 0;
+      foreach (RealVector r in front) {
+        d[i] = minDistance(r, front);
+        sum += d[i++];
+      if (front == null) throw new ArgumentException("Fronts must not be null");
+      List<double> d = new List<double>();
+      foreach (double[] r in front) {
+        double dist = Utilities.minDistance(r, front, false);
+        d.Add(dist>=0?dist:0);
+      }
+      int n = d.Count;
+      if (n == 0) throw new ArgumentException("Fronts must not be empty");
+      return Math.Sqrt(d.Variance()*(n-1)/n);
+      }
-      double mean = sum / front.Length;
-      sum = 0;
-      foreach (double e in d) {
-        sum += (e - mean) * (e - mean);
+      }
-      sum /= front.Length;
-      return Math.Sqrt(sum);
+    }
-    private double minDistance(RealVector point, RealVector[] list) {
-      //TODO inefficient
-      double min = Double.MaxValue;
-      foreach (RealVector r in list) {
-        if (r == point) continue;
-        double d = 0;
-        for (int i = 0; i < r.Length; i++) {
-          d += (point[i] - r[i]) * (point[i] - r[i]);
+        }
-        min = Math.Min(d, min);
+      }
-      return Math.Sqrt(min);
+    }

branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/Interfaces/IMultiObjectiveDistance.cs

-                      r13562
+                      r13620
 using HeuristicLab.Encodings.RealVectorEncoding;
+using System.Collections.Generic;
 namespace HeuristicLab.Problems.MultiObjectiveTestFunctions {
 …
     /// <param name="optimalFront">an array of 2-dimensional? RealVectors that denote the optimal Pareto front for a given Problem</param>
     /// <returns></returns>
     double Compare(RealVector[] front, RealVector[] optimalFront);
+    double Compare(IEnumerable<double[]> front, IEnumerable<double[]> optimalFront);
+  }

branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/Interfaces/IMultiObjectiveTestFunction.cs

-                      r13515
+                      r13620
 #endregion
+using System.Collections.Generic;
 using HeuristicLab.Core;
-using HeuristicLab.Data;
 using HeuristicLab.Encodings.RealVectorEncoding;
 …
   /// </summary>
   public interface IMultiObjectiveTestFunction : INamedItem {
+    bool[] Maximization { get; }
+    DoubleMatrix Bounds { get; }
+    int MinimumProblemSize { get; }
+    int MaximumProblemSize { get; }
+    int MinimumSolutionSize { get; }
+    int MaximumSolutionSize { get; }
+    int ActualSolutionSize { get; set; }
+    bool[] Maximization(int objectives);
+    double[,] Bounds(int objectives);
+    IEnumerable<double[]> OptimalParetoFront(int objectives);
+    double[] ReferencePoint(int objectives);
+    double BestKnownHypervolume(int objectives);
+    int MinimumSolutionLength { get; }
+    int MaximumSolutionLength { get; }
+    int MinimumObjectives { get; }
+    int MaximumObjectives { get; }
+    RealVector[] OptimalParetoFront { get; }
+    RealVector ReferencePoint { get; }
+    double BestKnownHypervolume { get; }
+    double[] Evaluate(RealVector point);
+    double[] Evaluate(RealVector point, int objectives);
+  }
+}

branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/MultiObjectiveTestFunctionProblem.cs

-                      r13515
+                      r13620
 using System;
+using System.Collections.Generic;
 using HeuristicLab.Common;
 using HeuristicLab.Core;
 …
 using HeuristicLab.Parameters;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Problems.Instances;
 using HeuristicLab.Problems.MultiObjectiveTestFunctions;
+using HeuristicLab.Problems.MultiObjectiveTestFunctions.Drawings;
 namespace HeuristicLab.Problems.MultiObjectiveTestFunction {
   [StorableClass]
   public class MultiObjectiveTestFunctionProblem : MultiObjectiveBasicProblem<RealVectorEncoding> {
+  public class MultiObjectiveTestFunctionProblem : MultiObjectiveBasicProblem<RealVectorEncoding>, IProblemInstanceConsumer<MOTFData> {
 …
     public override bool[] Maximization {
       get {
         return Parameters.ContainsKey("TestFunction") ? TestFunction.Maximization : new bool[2];
+        return Parameters.ContainsKey("TestFunction") ? TestFunction.Maximization(Objectives) : new bool[2];
+      }
+    }
 …
     #region Properties
     public int ProblemSize {
+    public int SolutionLength {
       get { return ProblemSizeParameter.Value.Value; }
       set { ProblemSizeParameter.Value.Value = value; }
+    }
     public int SolutionSize {
+    public int Objectives {
       get { return SolutionSizeParameter.Value.Value; }
       set { SolutionSizeParameter.Value.Value = value; }
 …
     public override void Analyze(Individual[] individuals, double[][] qualities, ResultCollection results, IRandom random) {
       base.Analyze(individuals, qualities, results, random);
+      if (qualities[0].Length != 2) { throw new Exception(); }
+      if (!results.ContainsKey("Hypervolume")) {
+        results.Add(new Result("Hypervolume", typeof(DoubleValue)));
+      }
+      if (!results.ContainsKey("BestKnownHypervolume")) {
+        results.Add(new Result("BestKnownHypervolume", typeof(DoubleValue)));
+      }
+      if (!results.ContainsKey("Absolute Distance to BestKnownHypervolume")) {
+        results.Add(new Result("Absolute Distance to BestKnownHypervolume", typeof(DoubleValue)));
+      }
+      Hypervolume comp = new Hypervolume(TestFunction.ReferencePoint, Maximization);
+      RealVector[] front = NonDominatedSelect.selectNonDominatedRows(qualities, Maximization, true);
+      double hv = comp.GetHypervolume(front);
+      double best = TestFunction.BestKnownHypervolume;
+      results["Hypervolume"].Value = new DoubleValue(hv);
+      results["BestKnownHypervolume"].Value = new DoubleValue(best);
+      results["Absolute Distance to BestKnownHypervolume"].Value = new DoubleValue(best - hv);
+      //if (qualities[0].Length != 2) { throw new Exception(); }
+      IEnumerable<double[]> opf = null;
+      try {
+        opf = TestFunction.OptimalParetoFront(Objectives);
+        //Genearational Distance
+        if (!results.ContainsKey("GenerationalDistance")) results.Add(new Result("GenerationalDistance", typeof(DoubleValue)));
+        GenerationalDistance gd = new GenerationalDistance(1);
+        results["GenerationalDistance"].Value = new DoubleValue(gd.Compare(qualities, opf));
+        //Inverted Generational Distance
+        if (!results.ContainsKey("InvertedGenerationalDistance")) results.Add(new Result("InvertedGenerationalDistance", typeof(DoubleValue)));
+        InvertedGenerationalDistance igd = new InvertedGenerationalDistance(1);
+        results["InvertedGenerationalDistance"].Value = new DoubleValue(igd.Compare(qualities, opf));
+      }
+      catch (NotImplementedException) { } // only do this if the optimal Front is known
+      //Graphical analysis
+      if (!results.ContainsKey("Front")) results.Add(new Result("Front", typeof(IMOQualities)));
+      results["Front"].Value = new IMOSolution(qualities, individuals, opf, Objectives);
+      //Hypervolume analysis
+      if (!results.ContainsKey("Hypervolume")) results.Add(new Result("Hypervolume", typeof(DoubleValue)));
+      IEnumerable<double[]> front = NonDominatedSelect.selectNonDominatedVectors(qualities, Maximization, true);
+      if (!results.ContainsKey("BestKnownHypervolume")) results.Add(new Result("BestKnownHypervolume", typeof(DoubleValue)));
+      if (!results.ContainsKey("Absolute Distance to BestKnownHypervolume")) results.Add(new Result("Absolute Distance to BestKnownHypervolume", typeof(DoubleValue)));
+      if (Objectives == 2) { //Hypervolume analysis only with 2 objectives for now
+        Hypervolume comp = new Hypervolume(TestFunction.ReferencePoint(Objectives), Maximization);
+        try {
+          double hv = comp.GetHypervolume(front);
+          results["Hypervolume"].Value = new DoubleValue(hv);
+          double best; double diff;
+          if (TestFunction.BestKnownHypervolume(Objectives) > 0) {   // if best HV is known at all
+            best = TestFunction.BestKnownHypervolume(Objectives);
+            diff = best - hv;
+            if (diff < 0) {  //replace best known Hypervolume
+              diff = 0;
+              best = hv;
+            }
+          } else {  //initalize best known Hypervolume
+            best = hv;
+            diff = 0;
+          }
+          results["BestKnownHypervolume"].Value = new DoubleValue(best);
+          results["Absolute Distance to BestKnownHypervolume"].Value = new DoubleValue(diff);
+        }
+        catch (ArgumentException) {
+          results["Hypervolume"].Value = new DoubleValue(Double.NaN);
+        }
+      }
+      //Experimental HV
+      if(Objectives != 2) {
+        FastHypervolume fcomp = new FastHypervolume(TestFunction.ReferencePoint(Objectives), Maximization);
+        try {
+          double hv = fcomp.GetHypervolume(front, TestFunction.Bounds(Objectives));
+          results["Hypervolume"].Value = new DoubleValue(hv);
+          double best; double diff;
+          if (TestFunction.BestKnownHypervolume(Objectives) > 0) {   // if best HV is known at all
+            best = TestFunction.BestKnownHypervolume(Objectives);
+            diff = best - hv;
+            if (diff < 0) {  //replace best known Hypervolume
+              diff = 0;
+              best = hv;
+            }
+          } else {  //initalize best known Hypervolume
+            best = hv;
+            diff = 0;
+          }
+          results["BestKnownHypervolume"].Value = new DoubleValue(best);
+          results["Absolute Distance to BestKnownHypervolume"].Value = new DoubleValue(diff);
+        }
+        catch (ArgumentException) {
+          results["Hypervolume"].Value = new DoubleValue(Double.NaN);
+        }
+      }
+      //Spacing analysis
+      if (!results.ContainsKey("Spacing")) results.Add(new Result("Spacing", typeof(DoubleValue)));
+      Spacing s = new Spacing();
+      results["Spacing"].Value = new DoubleValue(s.Get(qualities));
+      //Crowding
+      if (!results.ContainsKey("Crowding")) results.Add(new Result("Crowding", typeof(DoubleValue)));
+      Crowding c = new Crowding(TestFunction.Bounds(Objectives));
+      results["Crowding"].Value = new DoubleValue(c.Get(qualities));
+    }
 …
       RegisterEventHandlers();
+    }
     public override IDeepCloneable Clone(Cloner cloner) {
       return new MultiObjectiveTestFunctionProblem(this, cloner);
+    }
     [StorableHook(HookType.AfterDeserialization)]
     private void AfterDeserialization() {
 …
     public double[] Evaluate(RealVector individual, IRandom random) {
       return TestFunction.Evaluate(individual);
+      return TestFunction.Evaluate(individual, Objectives);
+    }
 …
+    }
+    public void Load(MOTFData data) {
+      TestFunction = data.Evaluator;
+    }
     #region Events
 …
     private void TestFunctionParameterOnValueChanged(object sender, EventArgs eventArgs) {
       var problemSizeChange = ProblemSize < TestFunction.MinimumProblemSize
                               || ProblemSize > TestFunction.MaximumProblemSize;
+      var problemSizeChange = SolutionLength < TestFunction.MinimumSolutionLength
+                              || SolutionLength > TestFunction.MaximumSolutionLength;
       if (problemSizeChange) {
         ProblemSize = Math.Max(TestFunction.MinimumProblemSize, Math.Min(ProblemSize, TestFunction.MaximumProblemSize));
+      }
       var solutionSizeChange = SolutionSize < TestFunction.MinimumSolutionSize
                               || SolutionSize > TestFunction.MaximumSolutionSize;
+        SolutionLength = Math.Max(TestFunction.MinimumSolutionLength, Math.Min(SolutionLength, TestFunction.MaximumSolutionLength));
+      }
+      var solutionSizeChange = Objectives < TestFunction.MinimumObjectives
+                              || Objectives > TestFunction.MaximumObjectives;
       if (solutionSizeChange) {
         ProblemSize = Math.Max(TestFunction.MinimumSolutionSize, Math.Min(SolutionSize, TestFunction.MaximumSolutionSize));
+      }
       Bounds = (DoubleMatrix)TestFunction.Bounds.Clone();
+        SolutionLength = Math.Max(TestFunction.MinimumObjectives, Math.Min(Objectives, TestFunction.MaximumObjectives));
+      }
+      Bounds = (DoubleMatrix)new DoubleMatrix(TestFunction.Bounds(Objectives)).Clone();
       OnReset();
+    }
     private void ProblemSizeOnValueChanged(object sender, EventArgs eventArgs) {
+      if (ProblemSize < TestFunction.MinimumProblemSize
+        || ProblemSize > TestFunction.MaximumProblemSize)
+        ProblemSize = Math.Min(TestFunction.MaximumProblemSize, Math.Max(TestFunction.MinimumProblemSize, ProblemSize));
+      if (SolutionSize < TestFunction.MinimumSolutionSize
+        || SolutionSize > TestFunction.MaximumSolutionSize)
+        SolutionSize = Math.Min(TestFunction.MaximumSolutionSize, Math.Max(TestFunction.MinimumSolutionSize, SolutionSize));
+      TestFunction.ActualSolutionSize = SolutionSize;
+      if (SolutionLength < TestFunction.MinimumSolutionLength
+        || SolutionLength > TestFunction.MaximumSolutionLength)
+        SolutionLength = Math.Min(TestFunction.MaximumSolutionLength, Math.Max(TestFunction.MinimumSolutionLength, SolutionLength));
+      if (Objectives < TestFunction.MinimumObjectives
+        || Objectives > TestFunction.MaximumObjectives)
+        Objectives = Math.Min(TestFunction.MaximumObjectives, Math.Max(TestFunction.MinimumObjectives, Objectives));
+    }
     private void SolutionSizeOnValueChanged(object sender, EventArgs eventArgs) {
+      if (SolutionSize < TestFunction.MinimumSolutionSize
+        || SolutionSize > TestFunction.MaximumSolutionSize)
+        SolutionSize = Math.Min(TestFunction.MaximumSolutionSize, Math.Max(TestFunction.MinimumSolutionSize, SolutionSize));
+      TestFunction.ActualSolutionSize = SolutionSize;
+      if (ProblemSize < TestFunction.MinimumProblemSize
+        || ProblemSize > TestFunction.MaximumProblemSize)
+        ProblemSize = Math.Min(TestFunction.MaximumProblemSize, Math.Max(TestFunction.MinimumProblemSize, ProblemSize));
+      if (Objectives < TestFunction.MinimumObjectives
+        || Objectives > TestFunction.MaximumObjectives)
+        Objectives = Math.Min(TestFunction.MaximumObjectives, Math.Max(TestFunction.MinimumObjectives, Objectives));
+      if (SolutionLength < TestFunction.MinimumSolutionLength
+        || SolutionLength > TestFunction.MaximumSolutionLength)
+        SolutionLength = Math.Min(TestFunction.MaximumSolutionLength, Math.Max(TestFunction.MinimumSolutionLength, SolutionLength));
+    }
 …
       //empty for now
+    }
     #endregion
+  }

branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/NonDominatedSelect.cs

-                      r13562
+                      r13620
 using System.Collections.Generic;
+using System.Linq;
 using HeuristicLab.Core;
-using HeuristicLab.Encodings.RealVectorEncoding;
 namespace HeuristicLab.Problems.MultiObjectiveTestFunction {
+namespace HeuristicLab.Problems.MultiObjectiveTestFunctions {
   public class NonDominatedSelect {
 …
     public static RealVector[] selectNonDominatedVectors(RealVector[] qualities, bool[] maximization, bool dominateOnEqualQualities) {
       int populationSize = qualities.Length;
+    public static IEnumerable<double[]> selectNonDominatedVectors(IEnumerable<double[]> qualities, bool[] maximization, bool dominateOnEqualQualities) {
+      int populationSize = qualities.Count();
       List<RealVector> front = new List<RealVector>();
       foreach (RealVector row in qualities) {
+      List<double[]> front = new List<double[]>();
+      foreach (double[] row in qualities) {
         bool insert = true;
         for (int i = 0; i < front.Count; i++) {
 …
+        }
         if (insert) {
           front.Add(new RealVector(row));
+          front.Add(row);
+        }
+      }
       return front.ToArray();
+      return front;
+    }
+    public static RealVector[] selectNonDominatedRows(double[][] qualities, bool[] maximization, bool dominateOnEqualQualities) {
+      int populationSize = qualities.Length;
+      List<RealVector> front = new List<RealVector>();
+      foreach (double[] row in qualities) {
+        bool insert = true;
+        for (int i = 0; i < front.Count; i++) {
+          DominationResult res = Dominates(front[i], row, maximization, dominateOnEqualQualities);
+          if (res == DominationResult.Dominates) { insert = false; break; }           //Vector domiates Row
+          else if (res == DominationResult.IsDominated) {   //Row dominates Vector
+            front.RemoveRange(i, 1);
+          }
+        }
+        if (insert) {
+          front.Add(new RealVector(row));
+        }
+      }
+      return front.ToArray();
+    }
+    private static DominationResult Dominates(RealVector left, double[] right, bool[] maximizations, bool dominateOnEqualQualities) {
+      //mkommend Caution: do not use LINQ.SequenceEqual for comparing the two quality arrays (left and right) due to performance reasons
+      if (dominateOnEqualQualities) {
+        var equal = true;
+        for (int i = 0; i < left.Length; i++) {
+          if (left[i] != right[i]) {
+            equal = false;
+            break;
+          }
+        }
+        if (equal) return DominationResult.Dominates;
+      }
+      bool leftIsBetter = false, rightIsBetter = false;
+      for (int i = 0; i < left.Length; i++) {
+        if (IsDominated(left[i], right[i], maximizations[i])) rightIsBetter = true;
+        else if (IsDominated(right[i], left[i], maximizations[i])) leftIsBetter = true;
+        if (leftIsBetter && rightIsBetter) break;
+      }
+      if (leftIsBetter && !rightIsBetter) return DominationResult.Dominates;
+      if (!leftIsBetter && rightIsBetter) return DominationResult.IsDominated;
+      return DominationResult.IsNonDominated;
+    }
+    private static DominationResult Dominates(RealVector left, RealVector right, bool[] maximizations, bool dominateOnEqualQualities) {
+    private static DominationResult Dominates(double[] left, double[] right, bool[] maximizations, bool dominateOnEqualQualities) {
       //mkommend Caution: do not use LINQ.SequenceEqual for comparing the two quality arrays (left and right) due to performance reasons
       if (dominateOnEqualQualities) {

branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/PFReader.cs

-                      r13562
+                      r13620
 using System;
 using System.Collections.Generic;
-using System.IO;
-using System.Linq;
-using System.Text;
-using System.Threading.Tasks;
-using HeuristicLab.Encodings.RealVectorEncoding;
 namespace HeuristicLab.Problems.MultiObjectiveTestFunctions {
 …
     //TODO exception handling
     public static RealVector[] getFromFile(String filename) {
+    public static IEnumerable<double[]> getFromFile(String filename) {
       double[] data = null;
       switch (filename) {
         case "Fonseca": data=new double[] { 0.000176662688, 0.9808196953, 0.0002622482087, 0.9804637139, 0.0004757736926, 0.9801036725, 0.0006892535714, 0.9797369958, 0.0009026878548, 0.9793635615, 0.001243925512, 0.9789859349, 0.001585046622, 0.9786013982, 0.001926051222, 0.9782098248, 0.002394641013, 0.9778139261, 0.002863010803, 0.9774108343, 0.003331160697, 0.977000419, 0.003926596353, 0.9765855441, 0.00452167628, 0.9761631856, 0.005116400691, 0.9757332084, 0.005838030675, 0.9752986372, 0.006559137231, 0.9748562836, 0.007279720739, 0.9744060083, 0.00812674948, 0.9739510039, 0.008973055502, 0.9734879105, 0.009818639422, 0.9730165844, 0.01079012882, 0.9725403942, 0.01176066507, 0.9720558005, 0.01273024911, 0.9715626548, 0.01382512033, 0.9710645104, 0.01491877735, 0.9705576398, 0.01601122151, 0.9700418902, 0.017228257, 0.9695210077, 0.01844378722, 0.9689910686, 0.01965781402, 0.9684519154, 0.02099565984, 0.967907496, 0.02233167993, 0.9673536817, 0.0236658768, 0.9667903103, 0.02512304517, 0.9662215409, 0.02657803872, 0.9656430303, 0.02803086072, 0.9650546119, 0.0296057328, 0.9644606651, 0.03117805314, 0.9638566234, 0.03274782586, 0.9632423151, 0.03443865486, 0.9626223504, 0.03612652816, 0.9619919293, 0.03781145094, 0.9613508752, 0.03961636531, 0.9607040392, 0.04141789396, 0.9600463777, 0.04321604321, 0.9593777095, 0.04513305022, 0.9587031367, 0.04704621631, 0.958017362, 0.0489555492, 0.9573201994, 0.05098253857, 0.956617013, 0.05300520776, 0.955902241, 0.05502356598, 0.9551756926, 0.05715831386, 0.9544430053, 0.05928823925, 0.9536983417, 0.06141335303, 0.952941506, 0.06365352609, 0.9521784208, 0.06588835242, 0.9514029616, 0.06811784477, 0.9506149279, 0.07046100449, 0.9498205391, 0.07279827248, 0.949013372, 0.07512966355, 0.9481932211, 0.07757327066, 0.9473666151, 0.0800104215, 0.9465268202, 0.08244113312, 0.9456736259, 0.08498255227, 0.9448138827, 0.0875169323, 0.9439405335, 0.09004429269, 0.9430533632, 0.0926807972, 0.942159557, 0.09530966269, 0.941251722, 0.09793091132, 0.9403296381, 0.1006596881, 0.9394008389, 0.1033802102, 0.9384575824, 0.1060925027, 0.9374996436, 0.1089106573, 0.9365349181, 0.1117199272, 0.9355553016, 0.1145203405, 0.9345605641, 0.1174249021, 0.9335589774, 0.1203199361, 0.9325420608, 0.1232054737, 0.9315095798, 0.1261934006, 0.9304701964, 0.1291711452, 0.9294150398, 0.1321387424, 0.9283438706, 0.1352069272, 0.9272657562, 0.1382642649, 0.926171421, 0.1413107939, 0.9250606207, 0.1444560691, 0.9239428437, 0.1475898234, 0.9228083941, 0.1507120992, 0.9216570234, 0.1539312422, 0.920498656, 0.1571381834, 0.9193231611, 0.1603329689, 0.9181302855, 0.1636227081, 0.9169304058, 0.1668995584, 0.9157129408, 0.1701635703, 0.9144776326, 0.1735205902, 0.9132353263, 0.1768640296, 0.9119749742, 0.1801939435, 0.9106963141, 0.1836148905, 0.9094106763, 0.1870215624, 0.9081065301, 0.1904140186, 0.906783609, 0.1938955065, 0.9054537456, 0.1973620229, 0.9041049098, 0.2008136321, 0.9027368309, 0.2043522477, 0.901361861, 0.2078751951, 0.8999674536, 0.2113825437, 0.8985533341, 0.2149748521, 0.8971323917, 0.218550797, 0.8956915464, 0.2221104526, 0.8942305195, 0.2257530033, 0.8927627555, 0.2293784974, 0.8912746234, 0.2329870147, 0.8897658404, 0.2366763464, 0.8882504249, 0.2403479324, 0.8867141766, 0.2440018582, 0.8851568092, 0.2477345046, 0.8835929333, 0.2514487216, 0.8820077613, 0.2551446002, 0.8804010033, 0.2589170954, 0.8787878814, 0.262670484, 0.8771530021, 0.2664048626, 0.875496072, 0.2702137463, 0.873832944, 0.2740028539, 0.8721475999, 0.2777722882, 0.8704397429, 0.2816141105, 0.8687258765, 0.2854354966, 0.8669893384, 0.2892365553, 0.8652298289, 0.2931078825, 0.8634645216, 0.2969581237, 0.8616760912, 0.3007873938, 0.8598642348, 0.3046848129, 0.8580468166, 0.3085605078, 0.8562058284, 0.3124145995, 0.8543409645, 0.3163347232, 0.8524708001, 0.3202324971, 0.8505766241, 0.3241080487, 0.8486581281, 0.3280475201, 0.8467346191, 0.3319640301, 0.8447866629, 0.3358577125, 0.8428139488, 0.3398132097, 0.8408365364, 0.3437451487, 0.8388342479, 0.3476536699, 0.8368067705, 0.3516219105, 0.8347749374, 0.3555660121, 0.832717807, 0.3594861217, 0.8306350644, 0.3634638669, 0.8285483379, 0.3674169094, 0.8264359011, 0.3713454026, 0.8242974371, 0.3753294618, 0.822155391, 0.3792882723, 0.8199872304, 0.3832219941, 0.8177926372, 0.3872092284, 0.8155948943, 0.3911706867, 0.8133706427, 0.3951065358, 0.8111195628, 0.399093862, 0.8088657975, 0.4030549046, 0.8065851399, 0.4069898368, 0.8042772689, 0.410974231, 0.8019672096, 0.4149318543, 0.7996298854, 0.4188628866, 0.7972649745, 0.4228413876, 0.7948984058, 0.4267926516, 0.7925042117, 0.4307168649, 0.7900820697, 0.4346865776, 0.7876588349, 0.4386286088, 0.7852076269, 0.4425431514, 0.7827281229, 0.4465012497, 0.7802481262, 0.4504312444, 0.7777398222, 0.454333335, 0.7752028877, 0.4582770646, 0.7726660967, 0.4621922915, 0.7701006784, 0.4660792217, 0.7675063099, 0.4700059031, 0.7649127576, 0.4739037061, 0.7622902733, 0.477772843, 0.7596385341, 0.4816798738, 0.7569883214, 0.4855576743, 0.7543088875, 0.4894064631, 0.7515999103, 0.4932913201, 0.7488932079, 0.4971466191, 0.7461570117, 0.500972585, 0.7433910004, 0.5048328265, 0.7406280509, 0.508663207, 0.7378353523, 0.5124639575, 0.7350125843, 0.516297225, 0.7321937045, 0.5201003534, 0.7293448381, 0.5238735795, 0.726465666, 0.5276775997, 0.7235912484, 0.5314512276, 0.7206866253, 0.535194706, 0.7177514791, 0.5389672918, 0.7148219942, 0.5427092575, 0.7118621039, 0.5464208515, 0.7088714927, 0.5501599037, 0.7058874904, 0.5538681332, 0.7028729027, 0.5575457942, 0.6998274162, 0.5612493022, 0.6967895276, 0.5649218105, 0.6937208941, 0.5685635786, 0.6906212045, 0.5722296217, 0.6875301434, 0.5758645135, 0.6844081988, 0.5794685184, 0.6812550624, 0.5830952663, 0.6781116267, 0.5866907364, 0.6749371907, 0.5902551984, 0.6717314487, 0.5938409115, 0.668536522, 0.5973952457, 0.6653105001, 0.6009184756, 0.6620530805, 0.6044615056, 0.6588076329, 0.6079730807, 0.6555310179, 0.6114534802, 0.6522229362, 0.61495227, 0.6489280257, 0.618419554, 0.6456018985, 0.6218556156, 0.6422442589, 0.6253086998, 0.638901032, 0.6287302515, 0.6355265626, 0.6321205588, 0.6321205588, 0.6355265626, 0.6287302515, 0.638901032, 0.6253086998, 0.6422442589, 0.6218556156, 0.6456018985, 0.618419554, 0.6489280257, 0.61495227, 0.6522229362, 0.6114534802, 0.6555310179, 0.6079730807, 0.6588076329, 0.6044615056, 0.6620530805, 0.6009184756, 0.6653105001, 0.5973952457, 0.668536522, 0.5938409115, 0.6717314487, 0.5902551984, 0.6749371907, 0.5866907364, 0.6781116267, 0.5830952663, 0.6812550624, 0.5794685184, 0.6844081988, 0.5758645135, 0.6875301434, 0.5722296217, 0.6906212045, 0.5685635786, 0.6937208941, 0.5649218105, 0.6967895276, 0.5612493022, 0.6998274162, 0.5575457942, 0.7028729027, 0.5538681332, 0.7058874904, 0.5501599037, 0.7088714927, 0.5464208515, 0.7118621039, 0.5427092575, 0.7148219942, 0.5389672918, 0.7177514791, 0.535194706, 0.7206866253, 0.5314512276, 0.7235912484, 0.5276775997, 0.726465666, 0.5238735795, 0.7293448381, 0.5201003534, 0.7321937045, 0.516297225, 0.7350125843, 0.5124639575, 0.7378353523, 0.508663207, 0.7406280509, 0.5048328265, 0.7433910004, 0.500972585, 0.7461570117, 0.4971466191, 0.7488932079, 0.4932913201, 0.7515999103, 0.4894064631, 0.7543088875, 0.4855576743, 0.7569883214, 0.4816798738, 0.7596385341, 0.477772843, 0.7622902733, 0.4739037061, 0.7649127576, 0.4700059031, 0.7675063099, 0.4660792217, 0.7701006784, 0.4621922915, 0.7726660967, 0.4582770646, 0.7752028877, 0.454333335, 0.7777398222, 0.4504312444, 0.7802481262, 0.4465012497, 0.7827281229, 0.4425431514, 0.7852076269, 0.4386286088, 0.7876588349, 0.4346865776, 0.7900820697, 0.4307168649, 0.7925042117, 0.4267926516, 0.7948984058, 0.4228413876, 0.7972649745, 0.4188628866, 0.7996298854, 0.4149318543, 0.8019672096, 0.410974231, 0.8042772689, 0.4069898368, 0.8065851399, 0.4030549046, 0.8088657975, 0.399093862, 0.8111195628, 0.3951065358, 0.8133706427, 0.3911706867, 0.8155948943, 0.3872092284, 0.8177926372, 0.3832219941, 0.8199872304, 0.3792882723, 0.822155391, 0.3753294618, 0.8242974371, 0.3713454026, 0.8264359011, 0.3674169094, 0.8285483379, 0.3634638669, 0.8306350644, 0.3594861217, 0.832717807, 0.3555660121, 0.8347749374, 0.3516219105, 0.8368067705, 0.3476536699, 0.8388342479, 0.3437451487, 0.8408365364, 0.3398132097, 0.8408365364, 0.3398132097, 0.8428139488, 0.3358577125, 0.8447866629, 0.3319640301, 0.8467346191, 0.3280475201, 0.8486581281, 0.3241080487, 0.8505766241, 0.3202324971, 0.8524708001, 0.3163347232, 0.8543409645, 0.3124145995, 0.8562058284, 0.3085605078, 0.8580468166, 0.3046848129, 0.8598642348, 0.3007873938, 0.8616760912, 0.2969581237, 0.8634645216, 0.2931078825, 0.8652298289, 0.2892365553, 0.8669893384, 0.2854354966, 0.8687258765, 0.2816141105, 0.8704397429, 0.2777722882, 0.8721475999, 0.2740028539, 0.873832944, 0.2702137463, 0.875496072, 0.2664048626, 0.8771530021, 0.262670484, 0.8787878814, 0.2589170954, 0.8804010033, 0.2551446002, 0.8820077613, 0.2514487216, 0.8835929333, 0.2477345046, 0.8851568092, 0.2440018582, 0.8867141766, 0.2403479324, 0.8882504249, 0.2366763464, 0.8897658404, 0.2329870147, 0.8912746234, 0.2293784974, 0.8927627555, 0.2257530033, 0.8942305195, 0.2221104526, 0.8956915464, 0.218550797, 0.8971323917, 0.2149748521, 0.8985533341, 0.2113825437, 0.8999674536, 0.2078751951, 0.901361861, 0.2043522477, 0.9027368309, 0.2008136321, 0.9041049098, 0.1973620229, 0.9054537456, 0.1938955065, 0.906783609, 0.1904140186, 0.9081065301, 0.1870215624, 0.9094106763, 0.1836148905, 0.9106963141, 0.1801939435, 0.9119749742, 0.1768640296, 0.9132353263, 0.1735205902, 0.9144776326, 0.1701635703, 0.9157129408, 0.1668995584, 0.9169304058, 0.1636227081, 0.9181302855, 0.1603329689, 0.9193231611, 0.1571381834, 0.920498656, 0.1539312422, 0.9216570234, 0.1507120992, 0.9228083941, 0.1475898234, 0.9239428437, 0.1444560691, 0.9250606207, 0.1413107939, 0.926171421, 0.1382642649, 0.9272657562, 0.1352069272, 0.9283438706, 0.1321387424, 0.9294150398, 0.1291711452, 0.9304701964, 0.1261934006, 0.9315095798, 0.1232054737, 0.9325420608, 0.1203199361, 0.9335589774, 0.1174249021, 0.9345605641, 0.1145203405, 0.9355553016, 0.1117199272, 0.9365349181, 0.1089106573, 0.9374996436, 0.1060925027, 0.9384575824, 0.1033802102, 0.9394008389, 0.1006596881, 0.9403296381, 0.09793091132, 0.941251722, 0.09530966269, 0.942159557, 0.0926807972, 0.9430533632, 0.09004429269, 0.9439405335, 0.0875169323, 0.9448138827, 0.08498255227, 0.9456736259, 0.08244113312, 0.9465268202, 0.0800104215, 0.9473666151, 0.07757327066, 0.9481932211, 0.07512966355, 0.949013372, 0.07279827248, 0.9498205391, 0.07046100449, 0.9506149279, 0.06811784477, 0.9514029616, 0.06588835242, 0.9521784208, 0.06365352609, 0.952941506, 0.06141335303, 0.9536983417, 0.05928823925, 0.9544430053, 0.05715831386, 0.9551756926, 0.05502356598, 0.955902241, 0.05300520776, 0.956617013, 0.05098253857, 0.9573201994, 0.0489555492, 0.958017362, 0.04704621631, 0.9587031367, 0.04513305022, 0.9593777095, 0.04321604321, 0.9600463777, 0.04141789396, 0.9607040392, 0.03961636531, 0.9613508752, 0.03781145094, 0.9619919293, 0.03612652816, 0.9626223504, 0.03443865486, 0.9632423151, 0.03274782586, 0.9638566234, 0.03117805314, 0.9644606651, 0.0296057328, 0.9650546119, 0.02803086072, 0.9656430303, 0.02657803872, 0.9662215409, 0.02512304517, 0.9667903103, 0.0236658768, 0.9673536817, 0.02233167993, 0.967907496, 0.02099565984, 0.9684519154, 0.01965781402, 0.9689910686, 0.01844378722, 0.9695210077, 0.017228257, 0.9700418902, 0.01601122151, 0.9705576398, 0.01491877735, 0.9710645104, 0.01382512033, 0.9715626548, 0.01273024911, 0.9720558005, 0.01176066507, 0.9725403942, 0.01079012882, 0.9730165844, 0.009818639422, 0.9734879105, 0.008973055502, 0.9739510039, 0.00812674948, 0.9744060083, 0.007279720739, 0.9748562836, 0.006559137231, 0.9752986372, 0.005838030675, 0.9757332084, 0.005116400691, 0.9761631856, 0.00452167628, 0.9765855441, 0.003926596353, 0.977000419, 0.003331160697, 0.9774108343, 0.002863010803, 0.9778139261, 0.002394641013, 0.9782098248, 0.001926051222, 0.9786013982, 0.001585046622, 0.9789859349, 0.001243925512, 0.9793635615, 0.0009026878548, 0.9797369958, 0.0006892535714, 0.9801036725, 0.0004757736926, 0.9804637139, 0.0002622482087, 0.9808196953, 0.000176662688, 0.9811691901, 9.106984058e-05, 0.9815123166, 5.469665695e-06, 5.469665695e-06, 0.9815123166, 9.106984058e-05, 0.9811691901, }; break;
         case "Kursawe": data = new double[] { -20, 8.180035271e-11, -19.01225297, -0.1080697151, -19.03029552, -0.07779104234, -19.04837418, -0.04902448937, -19.06648904, -0.02174786463, -19.06648904, -0.02174786463, -18.00915364, -3.810091895, -18.00915364, -3.810091895, -18.02518798, -3.777701496, -18.02518798, -3.777701496, -18.04125442, -3.739708509, -18.05735302, -3.696507436, -18.05735302, -3.696507436, -18.07348385, -3.648483493, -18.07348385, -3.648483493, -18.08964698, -3.59601178, -18.10584246, -3.53945657, -18.12207037, -3.479170725, -18.13833076, -3.415495219, -18.15462371, -3.348758763, -18.15462371, -3.348758763, -18.17094928, -3.279277522, -18.17094928, -3.279277522, -18.18730753, -3.207354924, -18.20369853, -3.133281537, -18.22012235, -3.057335028, -18.23657904, -2.979780177, -18.25306868, -2.900868953, -18.25306868, -2.900868953, -18.26959134, -2.820840641, -18.26959134, -2.820840641, -18.28614707, -2.739922013, -18.30273595, -2.658327536, -18.31935804, -2.576259622, 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-15.41305962, -8.497436278, -15.41436244, -8.490531378, -15.41806751, -8.482157403, -15.41842307, -8.470670937, -15.4227778, -8.463961543, -15.42720612, -8.447330305, -15.4307395, -8.42968569, -15.43133545, -8.427469864, -15.43553679, -8.4159376, -15.44003458, -8.399306362, -15.44424784, -8.38426879, -15.44816032, -8.366026555, -15.44827899, -8.363465887, -15.45286304, -8.35128242, -15.45714572, -8.336244847, -15.46114133, -8.322825482, -15.46483439, -8.306221769, -15.46567544, -8.298810706, -15.46823707, -8.291272936, -15.47004361, -8.288220905, -15.47410857, -8.274801539, -15.47788392, -8.263020695, -15.481355, -8.248071863, -15.48292615, -8.235749191, -15.48453327, -8.234790568, -15.48707581, -8.226777596, -15.48740652, -8.217977924, -15.49092042, -8.214996753, -15.49447293, -8.20487079, -15.49771946, -8.191589495, -15.50067077, -8.179984937, -15.50395693, -8.16697281, -15.50757859, -8.156846847, -15.51090566, -8.148388421, -15.51392507, -8.136783864, -15.51664689, -8.12686202, 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-15.59062906, -7.891203391, -15.59247463, -7.88801523, -15.59401115, -7.886498702, -15.59523236, -7.881436645, -15.59614251, -7.878026177, -15.59673787, -7.870652004, -15.59702055, -7.864902411, -15.60606629, -7.844814157, -15.60896147, -7.843443658, -15.60993682, -7.84003319, -15.61059907, -7.838261605, -15.61094537, -7.832512012, -15.6109771, -7.828371232, -15.61097733, -7.822365827, -15.61097798, -7.804342414, -15.62373114, -7.802040203, -15.62445822, -7.800268618, -15.62487019, -7.800121613, -15.6249651, -7.795980833, -15.63895311, -7.763590435, -15.89067205, -7.750942164, -15.90657862, -7.745055755, -15.9224852, -7.739169347, -15.92251704, -7.731917545, -15.93842362, -7.726031136, -15.95436203, -7.712892925, -15.95439394, -7.706061562, -15.97033236, -7.692923351, -15.97039624, -7.679676568, -15.98630269, -7.672953778, -15.98633466, -7.666538358, -15.98643058, -7.64728617, -14.44665867, -11.62641325, -14.46705912, -11.62052684, -14.47731848, -11.61464043, -14.48757784, 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-10.40232596, -14.95784851, -10.38028597, -14.96032097, -10.37279505, -14.96238275, -10.35430201, -14.96627097, -10.33825065, -14.96905927, -10.3370849, -14.97143626, -10.32477111, -14.97340107, -10.3018303, -14.97757568, -10.30025766, -14.98027003, -10.29388383, -14.98255155, -10.27674716, -14.98584023, -10.25733202, -14.98885269, -10.25705659, -14.99145176, -10.24585988, -14.99363648, -10.22427545, -14.99721097, -10.21933903, }; break;
+        case "Kursawe": data = new double[] { -20, 8.180035271e-11, -19.01225297, -0.1080697151, -19.03029552, -0.07779104234, -19.04837418, -0.04902448937, -19.06648904, -0.02174786463, -19.06648904, -0.02174786463, -18.00915364, -3.810091895, -18.00915364, -3.810091895, -18.02518798, -3.777701496, -18.02518798, -3.777701496, -18.04125442, -3.739708509, -18.05735302, -3.696507436, -18.05735302, -3.696507436, -18.07348385, -3.648483493, -18.07348385, -3.648483493, -18.08964698, -3.59601178, -18.10584246, -3.53945657, -18.12207037, -3.479170725, -18.13833076, -3.415495219, -18.15462371, -3.348758763, -18.15462371, -3.348758763, -18.17094928, -3.279277522, -18.17094928, -3.279277522, -18.18730753, -3.207354924, -18.20369853, -3.133281537, -18.22012235, -3.057335028, -18.23657904, -2.979780177, -18.25306868, -2.900868953, -18.25306868, -2.900868953, -18.26959134, -2.820840641, -18.26959134, -2.820840641, -18.28614707, -2.739922013, -18.30273595, -2.658327536, -18.31935804, -2.576259622, 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-10.40232596, -14.95784851, -10.38028597, -14.96032097, -10.37279505, -14.96238275, -10.35430201, -14.96627097, -10.33825065, -14.96905927, -10.3370849, -14.97143626, -10.32477111, -14.97340107, -10.3018303, -14.97757568, -10.30025766, -14.98027003, -10.29388383, -14.98255155, -10.27674716, -14.98584023, -10.25733202, -14.98885269, -10.25705659, -14.99145176, -10.24585988, -14.99363648, -10.22427545, -14.99721097, -10.21933903 }; break;
         case "SchafferN1": data = new double[] { 0.0003999999694, 3.920400003, 0.0008999999541, 3.880900003, 0.001599999939, 3.841600003, 0.002499999924, 3.802500003, 0.003599999908, 3.763600003, 0.004899999893, 3.724900003, 0.006399999878, 3.686400003, 0.008099999862, 3.648100003, 0.009999999847, 3.610000003, 0.01209999983, 3.572100003, 0.01439999982, 3.534400003, 0.0168999998, 3.496900003, 0.01959999979, 3.459600003, 0.02249999977, 3.422500003, 0.02559999976, 3.385600003, 0.02889999974, 3.348900003, 0.03239999972, 3.312400003, 0.03609999971, 3.276100003, 0.03999999969, 3.240000003, 0.04409999968, 3.204100003, 0.04839999966, 3.168400003, 0.05289999965, 3.132900003, 0.05759999963, 3.097600003, 0.06249999962, 3.062500003, 0.0675999996, 3.027600003, 0.07289999959, 2.992900003, 0.07839999957, 2.958400003, 0.08409999956, 2.924100003, 0.08999999954, 2.890000003, 0.09609999953, 2.856100003, 0.1023999995, 2.822400003, 0.1088999995, 2.788900003, 0.1155999995, 2.755600003, 0.1224999995, 2.722500003, 0.1295999994, 2.689600003, 0.1368999994, 2.656900002, 0.1443999994, 2.624400002, 0.1520999994, 2.592100002, 0.1599999994, 2.560000002, 0.1680999994, 2.528100002, 0.1763999994, 2.496400002, 0.1848999993, 2.464900002, 0.1935999993, 2.433600002, 0.2024999993, 2.402500002, 0.2115999993, 2.371600002, 0.2208999993, 2.340900002, 0.2303999993, 2.310400002, 0.2400999993, 2.280100002, 0.2499999992, 2.250000002, 0.2600999992, 2.220100002, 0.2703999992, 2.190400002, 0.2808999992, 2.160900002, 0.2915999992, 2.131600002, 0.3024999992, 2.102500002, 0.3135999991, 2.073600002, 0.3248999991, 2.044900002, 0.3363999991, 2.016400002, 0.3480999991, 1.988100002, 0.3599999991, 1.960000002, 0.3720999991, 1.932100002, 0.3843999991, 1.904400002, 0.396899999, 1.876900002, 0.409599999, 1.849600002, 0.422499999, 1.822500002, 0.435599999, 1.795600002, 0.448899999, 1.768900002, 0.462399999, 1.742400002, 0.4760999989, 1.716100002, 0.4899999989, 1.690000002, 0.5040999989, 1.664100002, 0.5183999989, 1.638400002, 0.5328999989, 1.612900002, 0.5475999989, 1.587600002, 0.5624999989, 1.562500002, 0.5775999988, 1.537600002, 0.5928999988, 1.512900002, 0.6083999988, 1.488400002, 0.6240999988, 1.464100002, 0.6399999988, 1.440000002, 0.6560999988, 1.416100002, 0.6723999987, 1.392400002, 0.6888999987, 1.368900002, 0.7055999987, 1.345600002, 0.7224999987, 1.322500002, 0.7395999987, 1.299600002, 0.7568999987, 1.276900002, 0.7743999987, 1.254400002, 0.7920999986, 1.232100002, 0.8099999986, 1.210000002, 0.8280999986, 1.188100002, 0.8463999986, 1.166400002, 0.8648999986, 1.144900002, 0.8835999986, 1.123600002, 0.9024999985, 1.102500002, 0.9215999985, 1.081600002, 0.9408999985, 1.060900002, 0.9603999985, 1.040400002, 0.9800999985, 1.020100002, 0.9999999985, 1.000000002, 1.020099998, 0.9801000015, 1.040399998, 0.9604000015, 1.060899998, 0.9409000015, 1.081599998, 0.9216000015, 1.102499998, 0.9025000015, 1.123599998, 0.8836000014, 1.144899998, 0.8649000014, 1.166399998, 0.8464000014, 1.188099998, 0.8281000014, 1.209999998, 0.8100000014, 1.232099998, 0.7921000014, 1.254399998, 0.7744000013, 1.276899998, 0.7569000013, 1.299599998, 0.7396000013, 1.322499998, 0.7225000013, 1.345599998, 0.7056000013, 1.368899998, 0.6889000013, 1.392399998, 0.6724000013, 1.416099998, 0.6561000012, 1.439999998, 0.6400000012, 1.464099998, 0.6241000012, 1.488399998, 0.6084000012, 1.512899998, 0.5929000012, 1.537599998, 0.5776000012, 1.562499998, 0.5625000011, 1.587599998, 0.5476000011, 1.612899998, 0.5329000011, 1.638399998, 0.5184000011, 1.664099998, 0.5041000011, 1.689999998, 0.4900000011, 1.716099998, 0.4761000011, 1.742399998, 0.462400001, 1.768899998, 0.448900001, 1.795599998, 0.435600001, 1.822499998, 0.422500001, 1.849599998, 0.409600001, 1.876899998, 0.396900001, 1.904399998, 0.3844000009, 1.932099998, 0.3721000009, 1.959999998, 0.3600000009, 1.988099998, 0.3481000009, 2.016399998, 0.3364000009, 2.044899998, 0.3249000009, 2.073599998, 0.3136000009, 2.102499998, 0.3025000008, 2.131599998, 0.2916000008, 2.160899998, 0.2809000008, 2.190399998, 0.2704000008, 2.220099998, 0.2601000008, 2.249999998, 0.2500000008, 2.280099998, 0.2401000007, 2.310399998, 0.2304000007, 2.340899998, 0.2209000007, 2.371599998, 0.2116000007, 2.402499998, 0.2025000007, 2.433599998, 0.1936000007, 2.464899998, 0.1849000007, 2.496399998, 0.1764000006, 2.528099998, 0.1681000006, 2.559999998, 0.1600000006, 2.592099998, 0.1521000006, 2.624399998, 0.1444000006, 2.656899998, 0.1369000006, 2.689599997, 0.1296000006, 2.722499997, 0.1225000005, 2.755599997, 0.1156000005, 2.788899997, 0.1089000005, 2.822399997, 0.1024000005, 2.856099997, 0.09610000047, 2.889999997, 0.09000000046, 2.924099997, 0.08410000044, 2.958399997, 0.07840000043, 2.992899997, 0.07290000041, 3.027599997, 0.0676000004, 3.062499997, 0.06250000038, 3.097599997, 0.05760000037, 3.132899997, 0.05290000035, 3.168399997, 0.04840000034, 3.204099997, 0.04410000032, 3.239999997, 0.04000000031, 3.276099997, 0.03610000029, 3.312399997, 0.03240000028, 3.348899997, 0.02890000026, 3.385599997, 0.02560000024, 3.422499997, 0.02250000023, 3.459599997, 0.01960000021, 3.496899997, 0.0169000002, 3.534399997, 0.01440000018, 3.572099997, 0.01210000017, 3.609999997, 0.01000000015, 3.648099997, 0.008100000138, 3.686399997, 0.006400000122, 3.724899997, 0.004900000107, 3.763599997, 0.003600000092, 3.802499997, 0.002500000076, 3.841599997, 0.001600000061, 3.880899997, 0.0009000000459, 3.920399997, 0.0004000000306, 3.960099997, 0.0001000000153, 3.999999997, 5.844575628e-019, 5.844598067e-019, 4.000000003, 9.999998471e-005, 3.960100003, }; break;
 …
+      }
       if (data == null) throw new NotImplementedException();
       RealVector[] front = new RealVector[data.Length / 2];
+      double[][] front = new double[data.Length / 2][];
       for(int i = 0; i < data.Length; i += 2) {
         front[i / 2] = new RealVector(2);
+        front[i / 2] = new double[2];
         front[i / 2][0] = data[i];
         front[i / 2][1] = data[i + 1];

branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/Testfunctions/Fonseca.cs

-                      r13515
+                      r13620
 using System;
+using System.Collections.Generic;
 using HeuristicLab.Common;
 using HeuristicLab.Core;
 …
   public class Fonseca : MultiObjectiveTestFunction {
+    public override DoubleMatrix Bounds {
+      get {
+        return new DoubleMatrix(new double[,] { { -4, 4 } });
+      }
+    public override double[,] Bounds(int objectives) {
+        return new double[,] { { -4, 4 } };
+    }
+    public override bool[] Maximization {
+      get {
+        return new bool[] { false, false };
+      }
+    public override bool[] Maximization(int objecitves) {
+      return new bool[2];
+    }
+    public override int MaximumProblemSize {
+      get {
+        return int.MaxValue;
+      }
+    public override int MaximumSolutionLength {
+      get {return int.MaxValue; }
+    }
+    public override int MaximumSolutionSize {
+      get {
+        return 2;
+      }
+    public override int MinimumSolutionLength {
+      get { return 1; }
+    }
+    public override int MinimumProblemSize {
+      get {
+        return 1;
+      }
+    public override int MinimumObjectives {
+      get { return 2;  }
+    }
+    public override int MinimumSolutionSize {
+      get {
+        return 2;
+      }
+    public override int MaximumObjectives {
+      get { return 2; }
+    }
-    public override int ActualSolutionSize {
-      get {
-        return 2;
+      }
+      set {
+      }
+    public override IEnumerable<double[]> OptimalParetoFront(int objectives) {
+        return PFStore.get(this.ItemName);
+    }
+    public override double BestKnownHypervolume(int objectives) {
+        return new Hypervolume(ReferencePoint(objectives), Maximization(2)).GetHypervolume(OptimalParetoFront(objectives));
+    }
+    public override RealVector[] OptimalParetoFront {
+      get {
+        return PFReader.getFromFile("Fonseca");
+      }
+    }
+    public override double BestKnownHypervolume {
+      get {
+        return new Hypervolume(base.ReferencePoint,Maximization).GetHypervolume(OptimalParetoFront) ;
+      }
+    public override double[] ReferencePoint(int objectives) {
+      return new double[] { 11, 11 };
+    }
 …
     protected Fonseca(bool deserializing) : base(deserializing) { }
     protected Fonseca(Fonseca original, Cloner cloner) : base(original, cloner) { }
-    public Fonseca() : base() { }
     public override IDeepCloneable Clone(Cloner cloner) {
       return new Fonseca(this, cloner);
+    }
+    public Fonseca() : base() { }
     public override double[] Evaluate(RealVector r) {
+    public override double[] Evaluate(RealVector r, int objectives) {
+      if (objectives != 2) throw new ArgumentException("The Fonseca problem must always have 2 objectives");
       double f0 = 0.0, aux = 1.0 / Math.Sqrt(r.Length);
 …
       return res;
+    }
+  }
+}

branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/Testfunctions/Kursawe.cs

-                      r13515
+                      r13620
 using System;
+using System.Collections.Generic;
 using HeuristicLab.Common;
 using HeuristicLab.Core;
 …
   public class Kursawe : MultiObjectiveTestFunction {
+    public override DoubleMatrix Bounds {
+      get {
+        return new DoubleMatrix(new double[,] { { -5, 5 } });
+      }
+    public override double[,] Bounds(int objectives) {
+      return new double[,] { { -5, 5 } };
+    }
+    public override bool[] Maximization {
+      get {
+        return new bool[] { false, false };
+      }
+    public override bool[] Maximization(int objecitves) {
+      return new bool[2];
+    }
+    public override int MaximumProblemSize {
+      get {
+        return int.MaxValue;
+      }
+    public override int MinimumObjectives {
+      get { return 2; }
+    }
+    public override int MaximumObjectives {
+      get {return 2;}
+    }
+    public override int MaximumSolutionSize {
+      get {
+        return 2;
+      }
+    public override int MinimumSolutionLength {
+      get {return 3;}
+    }
+    public override int MaximumSolutionLength {
+      get { return int.MaxValue; }
+    }
+    public override int MinimumProblemSize {
+      get {
+        return 3;
+      }
+    public override IEnumerable<double[]> OptimalParetoFront(int objecitves) {
+        return PFStore.get(this.ItemName);
+    }
+    public override int MinimumSolutionSize {
+      get {
+        return 2;
+      }
+    public override double BestKnownHypervolume(int objecitves) {
+        return new Hypervolume(ReferencePoint(objecitves), Maximization(2)).GetHypervolume(OptimalParetoFront(objecitves));
+    }
+    public override int ActualSolutionSize {
+      get {
+        return 2;
+      }
+      set {
+      }
+    }
+    public override RealVector[] OptimalParetoFront {
+      get {
+        return PFReader.getFromFile("Kursawe");
+      }
+    }
+    public override double BestKnownHypervolume {
+      get {
+        return new Hypervolume(base.ReferencePoint, Maximization).GetHypervolume(OptimalParetoFront);
+      }
+    public override double[] ReferencePoint(int objectives) {
+      return new double[] { 11, 11 };
+    }
 …
     protected Kursawe(bool deserializing) : base(deserializing) { }
     protected Kursawe(Kursawe original, Cloner cloner) : base(original, cloner) { }
-    public Kursawe() : base() { }
     public override IDeepCloneable Clone(Cloner cloner) {
       return new Kursawe(this, cloner);
+    }
+    public Kursawe() : base() { }
+    public override double[] Evaluate(RealVector r) {
+    public override double[] Evaluate(RealVector r, int objectives) {
+      if (objectives != 2) throw new ArgumentException("The Kursawe problem must always have 2 objectives");
       //objective 1
       double f0 = 0.0;

branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/Testfunctions/MultiObjectiveTestFunction.cs

-                      r13562
+                      r13620
 #endregion
 using System;
+using System.Collections.Generic;
 using HeuristicLab.Common;
 using HeuristicLab.Core;
-using HeuristicLab.Data;
 using HeuristicLab.Encodings.RealVectorEncoding;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 …
       double[] res = new double[size];
       for(int i =0; i< size; i++) {
         res[i] = maximization[i] ? Double.MinValue : Double.MaxValue;
+        res[i] = maximization[i] ? double.MinValue : double.MaxValue;
+      }
       return res;
 …
     /// Returns whether the actual function constitutes a maximization or minimization problem.
     /// </summary>
     public abstract bool[] Maximization { get; }
+    public abstract bool[] Maximization(int objectives);
     /// <summary>
     /// Gets the lower and upper bound of the function.
     /// </summary>
     public abstract DoubleMatrix Bounds { get; }
+    public abstract double[,] Bounds(int objectives);
     /// <summary>
     /// Gets the minimum problem size.
     /// </summary>
     public abstract int MinimumProblemSize { get; }
+    public abstract int MinimumSolutionLength { get; }
     /// <summary>
     /// Gets the maximum problem size.
     /// </summary>
+    public abstract int MaximumProblemSize { get; }
+    public abstract int MaximumSolutionLength { get; }
     /// <summary>
     /// Gets and sets the actual solution size.
     /// </summary>
+    public abstract int ActualSolutionSize { get; set; }
+    ///
+    [Storable]
+    private int objectives;
     /// <summary>
     /// Gets the minimum solution size.
     /// </summary>
     public abstract int MinimumSolutionSize { get; }
+    public abstract int MinimumObjectives { get; }
     /// <summary>
     /// Gets the maximum solution size.
     /// </summary>
     public abstract int MaximumSolutionSize { get; }
+    public abstract int MaximumObjectives { get; }
     /// <summary>
     /// retrieves the optimal pareto front (if known from a file)
     /// </summary>
     public abstract RealVector[] OptimalParetoFront { get; }
+    public abstract IEnumerable<double[]> OptimalParetoFront(int objectives);
 …
     /// returns a Reference Point for Hypervolume calculation (currently default=(11|11))
     /// </summary>
+    public virtual RealVector ReferencePoint {
+      get {
+        return new RealVector(new double[]{ 11,11});
+      }
+    }
+    public abstract double[] ReferencePoint(int objectives);
     /// <summary>
+    /// returns the best known Hypervolume for this Problem   (currently default=0)
+    /// TODO BestKnownHypervolume is never updated
+    /// returns the best known Hypervolume for this Problem   (currently default=-1)
     /// </summary>
+    public virtual double BestKnownHypervolume {
+      get {
+        return 0;
+      }
+    }
+    public virtual double BestKnownHypervolume(int objectives) {
+      return -1;
+    }
     [StorableConstructor]
     protected MultiObjectiveTestFunction(bool deserializing) : base(deserializing) { }
+    protected MultiObjectiveTestFunction(MultiObjectiveTestFunction original, Cloner cloner) : base(original, cloner) { }
+    protected MultiObjectiveTestFunction(MultiObjectiveTestFunction original, Cloner cloner) : base(original, cloner) {
+      this.objectives = original.objectives;
+    }
     protected MultiObjectiveTestFunction() : base() { }
 …
     /// <param name="point">N-dimensional point for which the test function should be evaluated.</param>
     /// <returns>The result values of the function at the given point.</returns>
     public abstract double[] Evaluate(RealVector point);
+    public abstract double[] Evaluate(RealVector point, int objectives);
+  }

branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/Testfunctions/SchafferN1.cs

-                      r13515
+                      r13620
+using HeuristicLab.Common;
+using System;
+using System.Collections.Generic;
+using HeuristicLab.Common;
 using HeuristicLab.Core;
 using HeuristicLab.Data;
 …
   public class SchafferN1 : MultiObjectiveTestFunction {
+    public override DoubleMatrix Bounds {
+      get {
+        return new DoubleMatrix(new double[,] { { -1e5, 1e5 } });
+      }
+    public override double[,] Bounds(int objectives) {
+      return new double[,] { { -1e5, 1e5 } };
+    }
+    public override bool[] Maximization {
+      get {
+        return new bool[] { false, false };
+      }
+    public override bool[] Maximization(int objecitves) {
+      return new bool[2];
+    }
+    public override int MaximumProblemSize {
+      get {
+        return 1;
+      }
+    public override int MinimumSolutionLength {
+      get { return 1; }
+    }
+    public override int MaximumSolutionLength {
+      get { return 1; }
+    }
+    public override int MaximumSolutionSize {
+      get {
+        return 2;
+      }
+    public override int MinimumObjectives {
+      get { return 2; }
+    }
+    public override int MinimumProblemSize {
+      get {
+        return 1;
+      }
+    public override int MaximumObjectives {
+      get { return 2; }
+    }
+    public override int MinimumSolutionSize {
+      get {
+        return 2;
+      }
+    public override double[] ReferencePoint(int objecitves) {
+        return new double[] { 1e5, 1e5 };
+    }
-    public override int ActualSolutionSize {
-      get {
-        return 2;
+      }
       set {
+      }
+    public override IEnumerable<double[]> OptimalParetoFront(int objecitves) {
+        return PFStore.get("Schaffer");
+    }
+    public override double BestKnownHypervolume(int objecitves) {
+        return new Hypervolume(ReferencePoint(objecitves), Maximization(2)).GetHypervolume(OptimalParetoFront(objecitves));
-    public override RealVector[] OptimalParetoFront {
-      get {
-        return PFReader.getFromFile("SchafferN1");
+      }
+    }
-    public override double BestKnownHypervolume {
-      get {
-        return new Hypervolume(base.ReferencePoint, Maximization).GetHypervolume(OptimalParetoFront);
+      }
+    }
 …
     protected SchafferN1(bool deserializing) : base(deserializing) { }
     protected SchafferN1(SchafferN1 original, Cloner cloner) : base(original, cloner) { }
-    public SchafferN1() : base() { }
     public override IDeepCloneable Clone(Cloner cloner) {
       return new SchafferN1(this, cloner);
+    }
+    public SchafferN1() : base() { }
+    public override double[] Evaluate(RealVector r) {
+    public override double[] Evaluate(RealVector r, int objectives) {
+      if (objectives != 2) throw new ArgumentException("The Schaffer N1 problem must always have 2 objectives");
       if (r.Length != 1) return null;
       double x = r[0];
 …
       f1 *= f1;
      return new double[] { f0, f1 };
+      return new double[] { f0, f1 };
+    }
+  }

branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/Testfunctions/SchafferN2.cs

-                      r13515
+                      r13620
 using System;
+using System.Collections.Generic;
 using HeuristicLab.Common;
 using HeuristicLab.Core;
 …
   public class SchafferN2 : MultiObjectiveTestFunction {
+    public override DoubleMatrix Bounds {
+      get {
+        return new DoubleMatrix(new double[,] { { -5, 10 } });
+      }
+    public override double[,] Bounds(int objectives) {
+      return new double[,] { { -5, 10 } };
+    }
+    public override bool[] Maximization {
+      get {
+        return new bool[] { false, false };
+      }
+    public override bool[] Maximization(int objecitves) {
+      return new bool[2];
+    }
+    public override int MaximumProblemSize {
+      get {
+        return 1;
+      }
+    public override int MinimumSolutionLength {
+      get { return 1; }
+    }
+    public override int MaximumSolutionSize {
+      get {
+        return 2;
+      }
+    public override int MaximumSolutionLength {
+      get { return 1; }
+    }
+    public override int MinimumProblemSize {
+      get {
+        return 1;
+      }
+    public override int MinimumObjectives {
+      get { return 2; }
+    }
+    public override int MinimumSolutionSize {
+      get {
+        return 2;
+      }
+    public override int MaximumObjectives {
+      get { return 2; }
+    }
+    public override int ActualSolutionSize {
+      get {
+        return 2;
+      }
+      set {
+      }
+    public override double[] ReferencePoint(int objecitves) {
+      return new double[] { 100, 100 };
+    }
+    public override RealVector[] OptimalParetoFront {
+      get {
+        throw new NotImplementedException();
+      }
+    public override IEnumerable<double[]> OptimalParetoFront(int objectives) {
+      throw new NotImplementedException();
+    }
 …
     protected SchafferN2(bool deserializing) : base(deserializing) { }
     protected SchafferN2(SchafferN2 original, Cloner cloner) : base(original, cloner) { }
-    public SchafferN2() : base() { }
     public override IDeepCloneable Clone(Cloner cloner) {
       return new SchafferN2(this, cloner);
+    }
+    public SchafferN2() : base() { }
+    public override double[] Evaluate(RealVector r) {
+    public override double[] Evaluate(RealVector r, int objectives) {
+      if (objectives != 2) throw new ArgumentException("The Schaffer N1 problem must always have 2 objectives");
       double x = r[0];

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branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/Comparators/Crowding.cs

branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/Comparators/GenerationalDistance.cs

branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/Comparators/HyperVolume.cs

branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/Comparators/HyperVolumeFast.cs

branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/Comparators/InvertedGenerationalDistance.cs

branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/Comparators/Spacing.cs

branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/Interfaces/IMultiObjectiveDistance.cs

branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/Interfaces/IMultiObjectiveTestFunction.cs

branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/MultiObjectiveTestFunctionProblem.cs

branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/NonDominatedSelect.cs

branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/PFReader.cs

branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/Testfunctions/Fonseca.cs

branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/Testfunctions/Kursawe.cs

branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/Testfunctions/MultiObjectiveTestFunction.cs

branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/Testfunctions/SchafferN1.cs

branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/Testfunctions/SchafferN2.cs

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