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# Changeset 13620

Ignore:
Timestamp:
02/15/16 17:19:34 (8 years ago)
Message:

#1087 regorganized testfunctions, added view for qualities

Location:
branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3
Files:
12 deleted
16 edited

Unmodified
Removed
• ## branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/Comparators/Crowding.cs

 r13562 ﻿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 { /// /// 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 /// 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 front, IEnumerable optimalFront, double[,] bounds) { return new Crowding(bounds).Compare(front, optimalFront); } public static double GetCrowding(RealVector[] front, double[][] bounds) { public static double GetCrowding(IEnumerable front, double[,] bounds) { return new Crowding(bounds).Get(front); } public double Get(RealVector[] front) { public double Get(IEnumerable 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 front, IEnumerable optimalFront) { return Get(optimalFront) - Get(front); } private double CalcCrowding(RealVector point, RealVector[] list) { private double CalcCrowding(double[] point, IEnumerable 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(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 { private int dim; private int descending; public DimensionComparer(int dimension, bool descending) { this.dim = dimension; this.descending = descending ? -1 : 1; } #region IComparer 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 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(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 ﻿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 front, IEnumerable 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 front, IEnumerable 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 ﻿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 front, IEnumerable optimalFront) { return GetHypervolume(optimalFront) - GetHypervolume(front); } public double GetHypervolume(RealVector[] front) { public double GetHypervolume(IEnumerable 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(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(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 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 front, IEnumerable 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 { private int dim; private int descending; public DimensionComparer(int dimension, bool descending) { this.dim = dimension; this.descending = descending ? -1 : 1; } #region IComparer 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 ﻿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 front, IEnumerable optimalFront, double[,] bounds) { return GetHypervolume(optimalFront, bounds) - GetHypervolume(front, bounds); /// also called region /// public double GetHypervolume(RealVector[] front, double[][] bounds) { public double GetHypervolume(IEnumerable front, double[,] bounds) { //TODO what to do if set contains dominated points var list = new List(); if (front == null) throw new ArgumentException("Fronts must not be null"); var list = new List(); 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 points, int split, double cover, ExPrivates results) { private void GetHV(double[][] region, List 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(); var nonIntersect = new List(); 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(); for (int i = 1; i < coverIndex; i++) { if (partCovers(points[i], regionC, results)) move(points[i], pointsC); var pointsC = new List(); 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(); pointsC = new List(); 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 pointsC, List 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 points, int i, List 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 front, double[] reference, bool[] maximization, double[,] bounds) { FastHypervolume comp = new FastHypervolume(reference, maximization); return comp.GetHypervolume(front,bounds); } public static double GetDistance(IEnumerable front, IEnumerable 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 { private int dim; private int descending; public DimensionComparer(int dimension, bool descending) { this.dim = dimension; this.descending = descending ? -1 : 1; } #region IComparer 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 ﻿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; } /// /// /// public static double GetDistance(RealVector[] front, RealVector[] optimalFront, double p) { public static double GetDistance(IEnumerable front, IEnumerable 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 front, IEnumerable 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 ﻿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 front) { return new Spacing().Get(front); } public static double GetDistance(RealVector[] front, RealVector[] optimalFront) { public static double GetDistance(IEnumerable front, IEnumerable optimalFront) { return new Spacing().Get(front); } public double Compare(RealVector[] front, RealVector[] optimalFront) { public double Compare(IEnumerable front, IEnumerable optimalFront) { return GetSpacing(front) - GetSpacing(optimalFront); } public double Get(RealVector[] front) { public double Get(IEnumerable 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 d = new List(); 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 ﻿using HeuristicLab.Encodings.RealVectorEncoding; ﻿using System.Collections.Generic; namespace HeuristicLab.Problems.MultiObjectiveTestFunctions { /// an array of 2-dimensional? RealVectors that denote the optimal Pareto front for a given Problem /// double Compare(RealVector[] front, RealVector[] optimalFront); double Compare(IEnumerable front, IEnumerable optimalFront); }
• ## branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/Interfaces/IMultiObjectiveTestFunction.cs

 r13515 #endregion using System.Collections.Generic; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Encodings.RealVectorEncoding; /// 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 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/NonDominatedSelect.cs

 r13562 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 selectNonDominatedVectors(IEnumerable qualities, bool[] maximization, bool dominateOnEqualQualities) { int populationSize = qualities.Count(); List front = new List(); foreach (RealVector row in qualities) { List front = new List(); 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 front = new List(); 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) {

 r13562 ﻿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 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, -18.3360134, -2.4939089, -18.35270211, -2.411454518, -18.36942423, -2.329064462, -18.38617983, -2.246895899, -18.38617983, -2.246895899, -18.40296898, -2.165095526, -18.40296898, -2.165095526, 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-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, 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-10.53675205, -14.92016275, -10.52629641, -14.92179254, -10.50432191, -14.92685818, -10.49719018, -14.92920558, -10.49355097, -14.93114592, -10.47827247, -14.93803149, -10.4591972, -14.94028455, -10.4503499, -14.94212909, -10.43024852, -14.94660962, -10.41827896, -14.94917623, -10.41599612, -14.95133365, -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, 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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 ﻿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 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 ﻿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 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 #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. /// public abstract bool[] Maximization { get; } public abstract bool[] Maximization(int objectives); /// /// Gets the lower and upper bound of the function. /// public abstract DoubleMatrix Bounds { get; } public abstract double[,] Bounds(int objectives); /// /// Gets the minimum problem size. /// public abstract int MinimumProblemSize { get; } public abstract int MinimumSolutionLength { get; } /// /// Gets the maximum problem size. /// public abstract int MaximumProblemSize { get; } public abstract int MaximumSolutionLength { get; } /// /// Gets and sets the actual solution size. /// public abstract int ActualSolutionSize { get; set; } /// [Storable] private int objectives; /// /// Gets the minimum solution size. /// public abstract int MinimumSolutionSize { get; } public abstract int MinimumObjectives { get; } /// /// Gets the maximum solution size. /// public abstract int MaximumSolutionSize { get; } public abstract int MaximumObjectives { get; } /// /// retrieves the optimal pareto front (if known from a file) /// public abstract RealVector[] OptimalParetoFront { get; } public abstract IEnumerable OptimalParetoFront(int objectives); /// returns a Reference Point for Hypervolume calculation (currently default=(11|11)) /// public virtual RealVector ReferencePoint { get { return new RealVector(new double[]{ 11,11}); } } public abstract double[] ReferencePoint(int objectives); /// /// 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) /// 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() { } /// N-dimensional point for which the test function should be evaluated. /// The result values of the function at the given point. 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 ﻿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 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 ﻿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 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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