Index: /trunk/sources/HeuristicLab 3.3.sln
===================================================================
--- /trunk/sources/HeuristicLab 3.3.sln	(revision 5142)
+++ /trunk/sources/HeuristicLab 3.3.sln	(revision 5143)
@@ -118,4 +118,5 @@
 		{7F038E9A-B91E-4F49-8C27-D47BC86F1220} = {7F038E9A-B91E-4F49-8C27-D47BC86F1220}
 		{F409DD9E-1E9C-4EB1-AA3A-9F6E987C6E58} = {F409DD9E-1E9C-4EB1-AA3A-9F6E987C6E58}
+		{15452B9F-A436-4139-8EE7-DA72FB8D3664} = {15452B9F-A436-4139-8EE7-DA72FB8D3664}
 		{4FE983A1-AAF8-4977-B9E2-52B3224510CB} = {4FE983A1-AAF8-4977-B9E2-52B3224510CB}
 		{30DD3DA2-CA57-49DD-A5BA-50CEF19DB45F} = {30DD3DA2-CA57-49DD-A5BA-50CEF19DB45F}
@@ -329,4 +330,6 @@
 EndProject
 Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "HeuristicLab.DebugEngine-3.3", "HeuristicLab.DebugEngine\3.3\HeuristicLab.DebugEngine-3.3.csproj", "{49F28045-7B5F-411B-8D59-16C846FA26E0}"
+EndProject
+Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "HeuristicLab.Algorithms.NSGA2-3.3", "HeuristicLab.Algorithms.NSGA2\3.3\HeuristicLab.Algorithms.NSGA2-3.3.csproj", "{15452B9F-A436-4139-8EE7-DA72FB8D3664}"
 EndProject
 Global
@@ -1483,4 +1486,16 @@
 		{49F28045-7B5F-411B-8D59-16C846FA26E0}.Release|x86.ActiveCfg = Release|x86
 		{49F28045-7B5F-411B-8D59-16C846FA26E0}.Release|x86.Build.0 = Release|x86
+		{15452B9F-A436-4139-8EE7-DA72FB8D3664}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{15452B9F-A436-4139-8EE7-DA72FB8D3664}.Debug|Any CPU.Build.0 = Debug|Any CPU
+		{15452B9F-A436-4139-8EE7-DA72FB8D3664}.Debug|x64.ActiveCfg = Debug|x64
+		{15452B9F-A436-4139-8EE7-DA72FB8D3664}.Debug|x64.Build.0 = Debug|x64
+		{15452B9F-A436-4139-8EE7-DA72FB8D3664}.Debug|x86.ActiveCfg = Debug|x86
+		{15452B9F-A436-4139-8EE7-DA72FB8D3664}.Debug|x86.Build.0 = Debug|x86
+		{15452B9F-A436-4139-8EE7-DA72FB8D3664}.Release|Any CPU.ActiveCfg = Release|Any CPU
+		{15452B9F-A436-4139-8EE7-DA72FB8D3664}.Release|Any CPU.Build.0 = Release|Any CPU
+		{15452B9F-A436-4139-8EE7-DA72FB8D3664}.Release|x64.ActiveCfg = Release|x64
+		{15452B9F-A436-4139-8EE7-DA72FB8D3664}.Release|x64.Build.0 = Release|x64
+		{15452B9F-A436-4139-8EE7-DA72FB8D3664}.Release|x86.ActiveCfg = Release|x86
+		{15452B9F-A436-4139-8EE7-DA72FB8D3664}.Release|x86.Build.0 = Release|x86
 	EndGlobalSection
 	GlobalSection(SolutionProperties) = preSolution
Index: /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/BasicMultiObjectiveQualityAnalyzer.cs
===================================================================
--- /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/BasicMultiObjectiveQualityAnalyzer.cs	(revision 5143)
+++ /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/BasicMultiObjectiveQualityAnalyzer.cs	(revision 5143)
@@ -0,0 +1,98 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Text;
+using HeuristicLab.Operators;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Core;
+using HeuristicLab.Data;
+using HeuristicLab.Optimization;
+using HeuristicLab.Parameters;
+using HeuristicLab.Common;
+
+namespace HeuristicLab.Algorithms.NSGA2 {
+  [Item("BasicMultiObjectiveSolutionAnalyzer", "Basic analyzer for multiobjective problems that collects and presents the current pareto front as double matrix.")]
+  [StorableClass]
+  public class BasicMultiObjectiveQualityAnalyzer : SingleSuccessorOperator, IAnalyzer {
+    public IScopeTreeLookupParameter<IntValue> RankParameter {
+      get { return (IScopeTreeLookupParameter<IntValue>)Parameters["Rank"]; }
+    }
+    public IScopeTreeLookupParameter<DoubleArray> QualitiesParameter {
+      get { return (IScopeTreeLookupParameter<DoubleArray>)Parameters["Qualities"]; }
+    }
+    public ILookupParameter<ResultCollection> ResultsParameter {
+      get { return (ILookupParameter<ResultCollection>)Parameters["Results"]; }
+    }
+
+    [StorableConstructor]
+    protected BasicMultiObjectiveQualityAnalyzer(bool deserializing) : base(deserializing) { }
+    protected BasicMultiObjectiveQualityAnalyzer(BasicMultiObjectiveQualityAnalyzer original, Cloner cloner) : base(original, cloner) { }
+    public BasicMultiObjectiveQualityAnalyzer() {
+      Parameters.Add(new ScopeTreeLookupParameter<IntValue>("Rank", "The rank of solution describes to which front it belongs."));
+      Parameters.Add(new ScopeTreeLookupParameter<DoubleArray>("Qualities", "The vector of qualities of each solution."));
+      Parameters.Add(new LookupParameter<ResultCollection>("Results", "The result collection to store the front to."));
+    }
+
+    public override IOperation Apply() {
+      ItemArray<IntValue> ranks = RankParameter.ActualValue;
+      ItemArray<DoubleArray> qualities = QualitiesParameter.ActualValue;
+      ResultCollection results = ResultsParameter.ActualValue;
+      
+      bool populationLevel = RankParameter.Depth == 1;
+
+      int objectives = qualities[0].Length;
+      int frontSize = ranks.Count(x => x.Value == 0);
+      ItemArray<IScope> paretoArchive = null;
+      if (populationLevel) paretoArchive = new ItemArray<IScope>(frontSize);
+
+      DoubleMatrix front = new DoubleMatrix(frontSize, objectives);
+      int counter = 0;
+      for (int i = 0; i < ranks.Length; i++) {
+        if (ranks[i].Value == 0) {
+          for (int k = 0; k < objectives; k++)
+            front[counter, k] = qualities[i][k];
+          if (populationLevel) {
+            paretoArchive[counter] = (IScope)ExecutionContext.Scope.SubScopes[i].Clone();
+          }
+          counter++;
+        }
+      }
+
+      if (results.ContainsKey("Pareto Front"))
+        results["Pareto Front"].Value = front;
+      else results.Add(new Result("Pareto Front", front));
+
+      if (populationLevel) {
+        if (results.ContainsKey("Pareto Archive"))
+          results["Pareto Archive"].Value = paretoArchive;
+        else results.Add(new Result("Pareto Archive", paretoArchive));
+      }
+      return base.Apply();
+    }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new BasicMultiObjectiveQualityAnalyzer(this, cloner);
+    }
+  }
+}
Index: /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/CrowdedComparisonSorter.cs
===================================================================
--- /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/CrowdedComparisonSorter.cs	(revision 5143)
+++ /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/CrowdedComparisonSorter.cs	(revision 5143)
@@ -0,0 +1,99 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using HeuristicLab.Core;
+using HeuristicLab.Data;
+using HeuristicLab.Operators;
+using HeuristicLab.Parameters;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Common;
+
+namespace HeuristicLab.Algorithms.NSGA2 {
+  /// <summary>
+  /// CrowdedComparisonSorter as described in: Deb, Pratap, Agrawal and Meyarivan, "A Fast and Elitist Multiobjective
+  /// Genetic Algorithm: NSGA-II", IEEE Transactions On Evolutionary Computation, Vol. 6, No. 2, April 2002
+  /// </summary>
+  [Item("CrowdedComparisonSorter", @"CrowdedComparisonSorter as described in: Deb, Pratap, Agrawal and Meyarivan, ""A Fast and Elitist Multiobjective
+Genetic Algorithm: NSGA-II"", IEEE Transactions On Evolutionary Computation, Vol. 6, No. 2, April 2002.")]
+  [StorableClass]
+  public class CrowdedComparisonSorter : SingleSuccessorOperator {
+
+    public IScopeTreeLookupParameter<IntValue> RankParameter {
+      get { return (IScopeTreeLookupParameter<IntValue>)Parameters["Rank"]; }
+    }
+
+    public IScopeTreeLookupParameter<DoubleValue> CrowdingDistanceParameter {
+      get { return (IScopeTreeLookupParameter<DoubleValue>)Parameters["CrowdingDistance"]; }
+    }
+
+    [StorableConstructor]
+    protected CrowdedComparisonSorter(bool deserializing) : base(deserializing) { }
+    protected CrowdedComparisonSorter(CrowdedComparisonSorter original, Cloner cloner) : base(original, cloner) { }
+    public CrowdedComparisonSorter() {
+      Parameters.Add(new ScopeTreeLookupParameter<IntValue>("Rank", "The rank of the solution."));
+      Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("CrowdingDistance", "The crowding distance of the solution."));
+    }
+
+    public override IOperation Apply() {
+      ItemArray<IntValue> ranks = RankParameter.ActualValue;
+      ItemArray<DoubleValue> distances = CrowdingDistanceParameter.ActualValue;
+      int size = ranks.Length;
+      int[] indices = Enumerable.Range(0, size).ToArray();
+
+      IScope[] scopes = ExecutionContext.Scope.SubScopes.ToArray();
+      Array.Sort(indices, scopes, new CustomComparer(ranks, distances));
+      ExecutionContext.Scope.SubScopes.Clear();
+      ExecutionContext.Scope.SubScopes.AddRange(scopes);
+      return base.Apply();
+    }
+
+    private class CustomComparer : IComparer<int> {
+      ItemArray<IntValue> ranks;
+      ItemArray<DoubleValue> distances;
+
+      public CustomComparer(ItemArray<IntValue> ranks, ItemArray<DoubleValue> distances) {
+        this.ranks = ranks;
+        this.distances = distances;
+      }
+
+      #region IComparer<int> Members
+
+      public int Compare(int x, int y) {
+        if (ranks[x].Value < ranks[y].Value) return -1;
+        else if (ranks[x].Value > ranks[y].Value) return 1;
+        else { // ranks are the same -> compare by distance
+          if (distances[x].Value > distances[y].Value) return -1;
+          else if (distances[x].Value < distances[y].Value) return 1;
+          else return 0; // same distance
+        }
+      }
+
+      #endregion
+    }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new CrowdedComparisonSorter(this, cloner);
+    }
+  }
+}
Index: /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/CrowdedTournamentSelector.cs
===================================================================
--- /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/CrowdedTournamentSelector.cs	(revision 5143)
+++ /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/CrowdedTournamentSelector.cs	(revision 5143)
@@ -0,0 +1,119 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System.Collections.Generic;
+using System.Linq;
+using HeuristicLab.Core;
+using HeuristicLab.Data;
+using HeuristicLab.Optimization;
+using HeuristicLab.Parameters;
+using HeuristicLab.Selection;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Common;
+
+namespace HeuristicLab.Algorithms.NSGA2 {
+  [Item("CrowdedTournamentSelector", "Selects solutions using tournament selection by using the partial order defined in Deb et al. 2002. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), pp. 182-197.")]
+  [StorableClass]
+  public class CrowdedTournamentSelector : Selector, IMultiObjectiveSelector, IStochasticOperator {
+    public ILookupParameter<BoolArray> MaximizationParameter {
+      get { return (ILookupParameter<BoolArray>)Parameters["Maximization"]; }
+    }
+    public IValueLookupParameter<IntValue> NumberOfSelectedSubScopesParameter {
+      get { return (IValueLookupParameter<IntValue>)Parameters["NumberOfSelectedSubScopes"]; }
+    }
+    public IValueParameter<BoolValue> CopySelectedParameter {
+      get { return (IValueParameter<BoolValue>)Parameters["CopySelected"]; }
+    }
+    public ILookupParameter<IRandom> RandomParameter {
+      get { return (ILookupParameter<IRandom>)Parameters["Random"]; }
+    }
+    public ILookupParameter<ItemArray<DoubleArray>> QualitiesParameter {
+      get { return (ILookupParameter<ItemArray<DoubleArray>>)Parameters["Qualities"]; }
+    }
+    public IScopeTreeLookupParameter<IntValue> RankParameter {
+      get { return (IScopeTreeLookupParameter<IntValue>)Parameters["Rank"]; }
+    }
+    public IScopeTreeLookupParameter<DoubleValue> CrowdingDistanceParameter {
+      get { return (IScopeTreeLookupParameter<DoubleValue>)Parameters["CrowdingDistance"]; }
+    }
+    public IValueLookupParameter<IntValue> GroupSizeParameter {
+      get { return (IValueLookupParameter<IntValue>)Parameters["GroupSize"]; }
+    }
+
+    public BoolValue CopySelected {
+      get { return CopySelectedParameter.Value; }
+      set { CopySelectedParameter.Value = value; }
+    }
+
+    [StorableConstructor]
+    protected CrowdedTournamentSelector(bool deserializing) : base(deserializing) { }
+    protected CrowdedTournamentSelector(CrowdedTournamentSelector original, Cloner cloner) : base(original, cloner) { }
+    public CrowdedTournamentSelector()
+      : base() {
+      Parameters.Add(new LookupParameter<BoolArray>("Maximization", "For each objective determines whether it should be maximized or minimized."));
+      Parameters.Add(new ValueLookupParameter<IntValue>("NumberOfSelectedSubScopes", "The number of sub-scopes that should be selected."));
+      Parameters.Add(new ValueParameter<BoolValue>("CopySelected", "True if the selected scopes are to be copied (cloned) otherwise they're moved."));
+      Parameters.Add(new LookupParameter<IRandom>("Random", "The random number generator."));
+      Parameters.Add(new ScopeTreeLookupParameter<DoubleArray>("Qualities", "The solutions' qualities vector."));
+      Parameters.Add(new ScopeTreeLookupParameter<IntValue>("Rank", "The solutions' domination rank."));
+      Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("CrowdingDistance", "The solutions' crowding distance values."));
+      Parameters.Add(new ValueLookupParameter<IntValue>("GroupSize", "The size of the group from which the best will be chosen.", new IntValue(2)));
+    }
+
+    protected override IScope[] Select(List<IScope> scopes) {
+      IRandom random = RandomParameter.ActualValue;
+      List<int> ranks = RankParameter.ActualValue.Select(x => x.Value).ToList();
+      List<double> crowdingDistance = CrowdingDistanceParameter.ActualValue.Select(x => x.Value).ToList();
+      int count = NumberOfSelectedSubScopesParameter.ActualValue.Value;
+      int groupSize = GroupSizeParameter.ActualValue.Value;
+      bool copy = CopySelected.Value;
+      IScope[] selected = new IScope[count];
+
+      for (int i = 0; i < count; i++) {
+        int best = random.Next(scopes.Count);
+        int index;
+        for (int j = 1; j < groupSize; j++) {
+          index = random.Next(scopes.Count);
+          if (ranks[best] > ranks[index]
+            || ranks[best] == ranks[index]
+              && crowdingDistance[best] < crowdingDistance[index]) {
+            best = index;
+          }
+        }
+
+        if (copy)
+          selected[i] = (IScope)scopes[best].Clone();
+        else {
+          selected[i] = scopes[best];
+          scopes.RemoveAt(best);
+          ranks.RemoveAt(best);
+          crowdingDistance.RemoveAt(best);
+        }
+      }
+
+      return selected;
+    }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new CrowdedTournamentSelector(this, cloner);
+    }
+  }
+}
Index: /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/CrowdingDistanceAssignment.cs
===================================================================
--- /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/CrowdingDistanceAssignment.cs	(revision 5143)
+++ /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/CrowdingDistanceAssignment.cs	(revision 5143)
@@ -0,0 +1,123 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using HeuristicLab.Core;
+using HeuristicLab.Data;
+using HeuristicLab.Operators;
+using HeuristicLab.Parameters;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Common;
+
+namespace HeuristicLab.Algorithms.NSGA2 {
+  [Item("CrowdingDistanceAssignment", "Calculates the crowding distances for each sub-scope as described in Deb et al. 2002. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), pp. 182-197.")]
+  [StorableClass]
+  public class CrowdingDistanceAssignment : SingleSuccessorOperator {
+
+    public ScopeTreeLookupParameter<DoubleArray> QualitiesParameter {
+      get { return (ScopeTreeLookupParameter<DoubleArray>)Parameters["Qualities"]; }
+    }
+
+    public ScopeTreeLookupParameter<DoubleValue> CrowdingDistanceParameter {
+      get { return (ScopeTreeLookupParameter<DoubleValue>)Parameters["CrowdingDistance"]; }
+    }
+
+    private void QualitiesParameter_DepthChanged(object sender, EventArgs e) {
+      CrowdingDistanceParameter.Depth = QualitiesParameter.Depth;
+    }
+
+    [StorableConstructor]
+    protected CrowdingDistanceAssignment(bool deserializing) : base(deserializing) { }
+    protected CrowdingDistanceAssignment(CrowdingDistanceAssignment original, Cloner cloner) : base(original, cloner) { }
+    public CrowdingDistanceAssignment() {
+      Parameters.Add(new ScopeTreeLookupParameter<DoubleArray>("Qualities", "The vector of quality values."));
+      Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("CrowdingDistance", "Sets the crowding distance in each sub-scope."));
+      AttachEventHandlers();
+    }
+
+    [StorableHook(HookType.AfterDeserialization)]
+    private void AttachEventHandlers() {
+      QualitiesParameter.DepthChanged += new EventHandler(QualitiesParameter_DepthChanged);
+    }
+
+    public static void Apply(DoubleArray[] qualities, DoubleValue[] distances) {
+      int populationSize = qualities.Length;
+      int objectiveCount = qualities[0].Length;
+      for (int m = 0; m < objectiveCount; m++) {
+        Array.Sort<DoubleArray, DoubleValue>(qualities, distances, new QualitiesComparer(m));
+
+        distances[0].Value = double.MaxValue;
+        distances[populationSize - 1].Value = double.MaxValue;
+
+        double minQuality = qualities[0][m];
+        double maxQuality = qualities[populationSize - 1][m];
+        for (int i = 1; i < populationSize - 1; i++) {
+          distances[i].Value += (qualities[i + 1][m] - qualities[i - 1][m]) / (maxQuality - minQuality);
+        }
+      }
+    }
+
+    public override IOperation Apply() {
+      DoubleArray[] qualities = QualitiesParameter.ActualValue.ToArray();
+      int populationSize = qualities.Length;
+      DoubleValue[] distances = new DoubleValue[populationSize];
+      for (int i = 0; i < populationSize; i++)
+        distances[i] = new DoubleValue(0);
+
+      CrowdingDistanceParameter.ActualValue = new ItemArray<DoubleValue>(distances);
+      
+      Apply(qualities, distances);
+
+      return base.Apply();
+    }
+
+    private void Initialize(ItemArray<DoubleValue> distances) {
+      for (int i = 0; i < distances.Length; i++) {
+        if (distances[i] == null) distances[i] = new DoubleValue(0);
+        else distances[i].Value = 0;
+      }
+    }
+
+    private class QualitiesComparer : IComparer<DoubleArray> {
+      private int index;
+
+      public QualitiesComparer(int index) {
+        this.index = index;
+      }
+
+      #region IComparer<DoubleArray> Members
+
+      public int Compare(DoubleArray x, DoubleArray y) {
+        if (x[index] < y[index]) return -1;
+        else if (x[index] > y[index]) return +1;
+        else return 0;
+      }
+
+      #endregion
+    }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new CrowdingDistanceAssignment(this, cloner);
+    }
+  }
+}
Index: /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/DefaultCrossover.cs
===================================================================
--- /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/DefaultCrossover.cs	(revision 5143)
+++ /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/DefaultCrossover.cs	(revision 5143)
@@ -0,0 +1,61 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using HeuristicLab.Core;
+using HeuristicLab.Operators;
+using HeuristicLab.Optimization;
+using HeuristicLab.Parameters;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Common;
+
+namespace HeuristicLab.Algorithms.NSGA2 {
+  [Item("DefaultCrossover", "This operator creates a parent by copying a single individual.")]
+  [StorableClass]
+  public class DefaultCrossover : SingleSuccessorOperator, ICrossover, IStochasticOperator {
+    public ILookupParameter<IRandom> RandomParameter {
+      get { return (ILookupParameter<IRandom>)Parameters["Random"]; }
+    }
+
+
+    [StorableConstructor]
+    protected DefaultCrossover(bool deserializing) : base(deserializing) { }
+    protected DefaultCrossover(DefaultCrossover original, Cloner cloner) : base(original, cloner) { }
+    public DefaultCrossover()
+      : base() {
+      Parameters.Add(new LookupParameter<IRandom>("Random", "The random number generator."));
+    }
+
+    public override IOperation Apply() {
+      IScope scope = ExecutionContext.Scope;
+      int index = RandomParameter.ActualValue.Next(scope.SubScopes.Count);
+      IScope child = scope.SubScopes[index];
+
+      foreach (IVariable var in child.Variables)
+        scope.Variables.Add((IVariable)var.Clone());
+
+      return base.Apply();
+    }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new DefaultCrossover(this, cloner);
+    }
+  }
+}
Index: /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/FastNonDominatedSort.cs
===================================================================
--- /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/FastNonDominatedSort.cs	(revision 5143)
+++ /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/FastNonDominatedSort.cs	(revision 5143)
@@ -0,0 +1,162 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+using HeuristicLab.Core;
+using HeuristicLab.Data;
+using HeuristicLab.Operators;
+using HeuristicLab.Parameters;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Common;
+
+namespace HeuristicLab.Algorithms.NSGA2 {
+  /// <summary>
+  /// FastNonDominatedSort as described in: Deb, Pratap, Agrawal and Meyarivan, "A Fast and Elitist Multiobjective
+  /// Genetic Algorithm: NSGA-II", IEEE Transactions On Evolutionary Computation, Vol. 6, No. 2, April 2002
+  /// </summary>
+  [Item("FastNonDominatedSort", @"FastNonDominatedSort as described in: Deb, Pratap, Agrawal and Meyarivan, ""A Fast and Elitist Multiobjective
+Genetic Algorithm: NSGA-II"", IEEE Transactions On Evolutionary Computation, Vol. 6, No. 2, April 2002")]
+  [StorableClass]
+  public class FastNonDominatedSort : SingleSuccessorOperator {
+    private enum DominationResult { Dominates, IsDominated, IsNonDominated };
+
+    public IValueLookupParameter<BoolArray> MaximizationParameter {
+      get { return (IValueLookupParameter<BoolArray>)Parameters["Maximization"]; }
+    }
+
+    public IScopeTreeLookupParameter<DoubleArray> QualitiesParameter {
+      get { return (IScopeTreeLookupParameter<DoubleArray>)Parameters["Qualities"]; }
+    }
+
+    public IScopeTreeLookupParameter<IntValue> RankParameter {
+      get { return (IScopeTreeLookupParameter<IntValue>)Parameters["Rank"]; }
+    }
+
+    [StorableConstructor]
+    protected FastNonDominatedSort(bool deserializing) : base(deserializing) { }
+    protected FastNonDominatedSort(FastNonDominatedSort original, Cloner cloner) : base(original, cloner) { }
+    public FastNonDominatedSort() {
+      Parameters.Add(new ValueLookupParameter<BoolArray>("Maximization", "Whether each objective is maximization or minimization."));
+      Parameters.Add(new ScopeTreeLookupParameter<DoubleArray>("Qualities", "The qualities of a solution.", 1));
+      Parameters.Add(new ScopeTreeLookupParameter<IntValue>("Rank", "The rank of a solution.", 1));
+    }
+
+    public override IOperation Apply() {
+      BoolArray maximization = MaximizationParameter.ActualValue;
+      ItemArray<DoubleArray> qualities = QualitiesParameter.ActualValue;
+      if (qualities == null) throw new InvalidOperationException(Name + ": No qualities found.");
+
+      IScope scope = ExecutionContext.Scope;
+      int populationSize = scope.SubScopes.Count;
+
+      List<ScopeList> fronts = new List<ScopeList>();
+      Dictionary<IScope, List<int>> dominatedScopes = new Dictionary<IScope, List<int>>();
+      int[] dominationCounter = new int[populationSize];
+      ItemArray<IntValue> rank = new ItemArray<IntValue>(populationSize);
+
+      for (int pI = 0; pI < populationSize - 1; pI++) {
+        IScope p = scope.SubScopes[pI];
+        if (!dominatedScopes.ContainsKey(p))
+          dominatedScopes[p] = new List<int>();
+        for (int qI = pI + 1; qI < populationSize; qI++) {
+          DominationResult test = Dominates(qualities[pI], qualities[qI], maximization);
+          if (test == DominationResult.Dominates) {
+            dominatedScopes[p].Add(qI);
+            dominationCounter[qI] += 1;
+          } else if (test == DominationResult.IsDominated) {
+            dominationCounter[pI] += 1;
+            if (!dominatedScopes.ContainsKey(scope.SubScopes[qI]))
+              dominatedScopes.Add(scope.SubScopes[qI], new List<int>());
+            dominatedScopes[scope.SubScopes[qI]].Add(pI);
+          }
+          if (pI == populationSize - 2
+            && qI == populationSize - 1
+            && dominationCounter[qI] == 0) {
+            rank[qI] = new IntValue(0);
+            AddToFront(scope.SubScopes[qI], fronts, 0);
+          }
+        }
+        if (dominationCounter[pI] == 0) {
+          rank[pI] = new IntValue(0);
+          AddToFront(p, fronts, 0);
+        }
+      }
+      int i = 0;
+      while (i < fronts.Count && fronts[i].Count > 0) {
+        ScopeList nextFront = new ScopeList();
+        foreach (IScope p in fronts[i]) {
+          if (dominatedScopes.ContainsKey(p)) {
+            for (int k = 0; k < dominatedScopes[p].Count; k++) {
+              int dominatedScope = dominatedScopes[p][k];
+              dominationCounter[dominatedScope] -= 1;
+              if (dominationCounter[dominatedScope] == 0) {
+                rank[dominatedScope] = new IntValue(i + 1);
+                nextFront.Add(scope.SubScopes[dominatedScope]);
+              }
+            }
+          }
+        }
+        i += 1;
+        fronts.Add(nextFront);
+      }
+
+      RankParameter.ActualValue = rank;
+      
+      scope.SubScopes.Clear();
+      
+      for (i = 0; i < fronts.Count; i++) {
+        Scope frontScope = new Scope("Front " + i);
+        foreach (var p in fronts[i])
+          frontScope.SubScopes.Add(p);
+        if (frontScope.SubScopes.Count > 0)
+          scope.SubScopes.Add(frontScope);
+      }
+      return base.Apply();
+    }
+
+    private DominationResult Dominates(DoubleArray left, DoubleArray right, BoolArray maximizations) {
+      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 bool IsDominated(double left, double right, bool maximization) {
+      return maximization && left < right
+        || !maximization && left > right;
+    }
+
+    private void AddToFront(IScope p, List<ScopeList> fronts, int i) {
+      if (i == fronts.Count) fronts.Add(new ScopeList());
+      fronts[i].Add(p);
+    }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new FastNonDominatedSort(this, cloner);
+    }
+  }
+}
Index: /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/HeuristicLab.Algorithms.NSGA2-3.3.csproj
===================================================================
--- /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/HeuristicLab.Algorithms.NSGA2-3.3.csproj	(revision 5143)
+++ /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/HeuristicLab.Algorithms.NSGA2-3.3.csproj	(revision 5143)
@@ -0,0 +1,199 @@
+﻿<?xml version="1.0" encoding="utf-8"?>
+<Project ToolsVersion="4.0" DefaultTargets="Build" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
+  <PropertyGroup>
+    <Configuration Condition=" '$(Configuration)' == '' ">Debug</Configuration>
+    <Platform Condition=" '$(Platform)' == '' ">AnyCPU</Platform>
+    <ProductVersion>9.0.30729</ProductVersion>
+    <SchemaVersion>2.0</SchemaVersion>
+    <ProjectGuid>{15452B9F-A436-4139-8EE7-DA72FB8D3664}</ProjectGuid>
+    <OutputType>Library</OutputType>
+    <AppDesignerFolder>Properties</AppDesignerFolder>
+    <RootNamespace>HeuristicLab.Algorithms.NSGA2</RootNamespace>
+    <AssemblyName>HeuristicLab.Algorithms.NSGA2-3.3</AssemblyName>
+    <TargetFrameworkVersion>v3.5</TargetFrameworkVersion>
+    <FileAlignment>512</FileAlignment>
+    <SignAssembly>true</SignAssembly>
+    <AssemblyOriginatorKeyFile>HeuristicLab.snk</AssemblyOriginatorKeyFile>
+    <FileUpgradeFlags>
+    </FileUpgradeFlags>
+    <OldToolsVersion>3.5</OldToolsVersion>
+    <UpgradeBackupLocation />
+    <PublishUrl>publish\</PublishUrl>
+    <Install>true</Install>
+    <InstallFrom>Disk</InstallFrom>
+    <UpdateEnabled>false</UpdateEnabled>
+    <UpdateMode>Foreground</UpdateMode>
+    <UpdateInterval>7</UpdateInterval>
+    <UpdateIntervalUnits>Days</UpdateIntervalUnits>
+    <UpdatePeriodically>false</UpdatePeriodically>
+    <UpdateRequired>false</UpdateRequired>
+    <MapFileExtensions>true</MapFileExtensions>
+    <ApplicationRevision>0</ApplicationRevision>
+    <ApplicationVersion>1.0.0.%2a</ApplicationVersion>
+    <IsWebBootstrapper>false</IsWebBootstrapper>
+    <UseApplicationTrust>false</UseApplicationTrust>
+    <BootstrapperEnabled>true</BootstrapperEnabled>
+  </PropertyGroup>
+  <PropertyGroup Condition=" '$(Configuration)|$(Platform)' == 'Debug|AnyCPU' ">
+    <DebugSymbols>true</DebugSymbols>
+    <DebugType>full</DebugType>
+    <Optimize>false</Optimize>
+    <OutputPath>bin\Debug\</OutputPath>
+    <DefineConstants>DEBUG;TRACE</DefineConstants>
+    <ErrorReport>prompt</ErrorReport>
+    <WarningLevel>4</WarningLevel>
+    <CodeAnalysisRuleSet>AllRules.ruleset</CodeAnalysisRuleSet>
+  </PropertyGroup>
+  <PropertyGroup Condition=" '$(Configuration)|$(Platform)' == 'Release|AnyCPU' ">
+    <DebugType>pdbonly</DebugType>
+    <Optimize>true</Optimize>
+    <OutputPath>bin\Release\</OutputPath>
+    <DefineConstants>TRACE</DefineConstants>
+    <ErrorReport>prompt</ErrorReport>
+    <WarningLevel>4</WarningLevel>
+    <CodeAnalysisRuleSet>AllRules.ruleset</CodeAnalysisRuleSet>
+  </PropertyGroup>
+  <PropertyGroup Condition=" '$(Configuration)|$(Platform)' == 'Debug|x86' ">
+    <DebugSymbols>true</DebugSymbols>
+    <OutputPath>bin\x86\Debug\</OutputPath>
+    <DefineConstants>DEBUG;TRACE</DefineConstants>
+    <DebugType>full</DebugType>
+    <PlatformTarget>x86</PlatformTarget>
+    <ErrorReport>prompt</ErrorReport>
+    <CodeAnalysisRuleSet>AllRules.ruleset</CodeAnalysisRuleSet>
+  </PropertyGroup>
+  <PropertyGroup Condition=" '$(Configuration)|$(Platform)' == 'Release|x86' ">
+    <OutputPath>bin\x86\Release\</OutputPath>
+    <DefineConstants>TRACE</DefineConstants>
+    <Optimize>true</Optimize>
+    <DebugType>pdbonly</DebugType>
+    <PlatformTarget>x86</PlatformTarget>
+    <ErrorReport>prompt</ErrorReport>
+    <CodeAnalysisRuleSet>AllRules.ruleset</CodeAnalysisRuleSet>
+  </PropertyGroup>
+  <PropertyGroup Condition=" '$(Configuration)|$(Platform)' == 'Debug|x64' ">
+    <DebugSymbols>true</DebugSymbols>
+    <OutputPath>bin\x64\Debug\</OutputPath>
+    <DefineConstants>DEBUG;TRACE</DefineConstants>
+    <DebugType>full</DebugType>
+    <PlatformTarget>x64</PlatformTarget>
+    <ErrorReport>prompt</ErrorReport>
+    <CodeAnalysisRuleSet>AllRules.ruleset</CodeAnalysisRuleSet>
+  </PropertyGroup>
+  <PropertyGroup Condition=" '$(Configuration)|$(Platform)' == 'Release|x64' ">
+    <OutputPath>bin\x64\Release\</OutputPath>
+    <DefineConstants>TRACE</DefineConstants>
+    <Optimize>true</Optimize>
+    <DebugType>pdbonly</DebugType>
+    <PlatformTarget>x64</PlatformTarget>
+    <ErrorReport>prompt</ErrorReport>
+    <CodeAnalysisRuleSet>AllRules.ruleset</CodeAnalysisRuleSet>
+  </PropertyGroup>
+  <ItemGroup>
+    <ProjectReference Include="..\..\HeuristicLab.Analysis\3.3\HeuristicLab.Analysis-3.3.csproj">
+      <Project>{887425B4-4348-49ED-A457-B7D2C26DDBF9}</Project>
+      <Name>HeuristicLab.Analysis-3.3</Name>
+    </ProjectReference>
+    <ProjectReference Include="..\..\HeuristicLab.Collections\3.3\HeuristicLab.Collections-3.3.csproj">
+      <Project>{958B43BC-CC5C-4FA2-8628-2B3B01D890B6}</Project>
+      <Name>HeuristicLab.Collections-3.3</Name>
+    </ProjectReference>
+    <ProjectReference Include="..\..\HeuristicLab.Common\3.3\HeuristicLab.Common-3.3.csproj">
+      <Project>{A9AD58B9-3EF9-4CC1-97E5-8D909039FF5C}</Project>
+      <Name>HeuristicLab.Common-3.3</Name>
+    </ProjectReference>
+    <ProjectReference Include="..\..\HeuristicLab.Core\3.3\HeuristicLab.Core-3.3.csproj">
+      <Project>{C36BD924-A541-4A00-AFA8-41701378DDC5}</Project>
+      <Name>HeuristicLab.Core-3.3</Name>
+    </ProjectReference>
+    <ProjectReference Include="..\..\HeuristicLab.Data\3.3\HeuristicLab.Data-3.3.csproj">
+      <Project>{BBAB9DF5-5EF3-4BA8-ADE9-B36E82114937}</Project>
+      <Name>HeuristicLab.Data-3.3</Name>
+    </ProjectReference>
+    <ProjectReference Include="..\..\HeuristicLab.Operators\3.3\HeuristicLab.Operators-3.3.csproj">
+      <Project>{23DA7FF4-D5B8-41B6-AA96-F0561D24F3EE}</Project>
+      <Name>HeuristicLab.Operators-3.3</Name>
+    </ProjectReference>
+    <ProjectReference Include="..\..\HeuristicLab.Optimization.Operators\3.3\HeuristicLab.Optimization.Operators-3.3.csproj">
+      <Project>{25087811-F74C-4128-BC86-8324271DA13E}</Project>
+      <Name>HeuristicLab.Optimization.Operators-3.3</Name>
+    </ProjectReference>
+    <ProjectReference Include="..\..\HeuristicLab.Optimization\3.3\HeuristicLab.Optimization-3.3.csproj">
+      <Project>{14AB8D24-25BC-400C-A846-4627AA945192}</Project>
+      <Name>HeuristicLab.Optimization-3.3</Name>
+    </ProjectReference>
+    <ProjectReference Include="..\..\HeuristicLab.Parameters\3.3\HeuristicLab.Parameters-3.3.csproj">
+      <Project>{56F9106A-079F-4C61-92F6-86A84C2D84B7}</Project>
+      <Name>HeuristicLab.Parameters-3.3</Name>
+    </ProjectReference>
+    <ProjectReference Include="..\..\HeuristicLab.Persistence\3.3\HeuristicLab.Persistence-3.3.csproj">
+      <Project>{102BC7D3-0EF9-439C-8F6D-96FF0FDB8E1B}</Project>
+      <Name>HeuristicLab.Persistence-3.3</Name>
+    </ProjectReference>
+    <ProjectReference Include="..\..\HeuristicLab.PluginInfrastructure\3.3\HeuristicLab.PluginInfrastructure-3.3.csproj">
+      <Project>{94186A6A-5176-4402-AE83-886557B53CCA}</Project>
+      <Name>HeuristicLab.PluginInfrastructure-3.3</Name>
+    </ProjectReference>
+    <ProjectReference Include="..\..\HeuristicLab.Random\3.3\HeuristicLab.Random-3.3.csproj">
+      <Project>{F4539FB6-4708-40C9-BE64-0A1390AEA197}</Project>
+      <Name>HeuristicLab.Random-3.3</Name>
+    </ProjectReference>
+    <ProjectReference Include="..\..\HeuristicLab.Selection\3.3\HeuristicLab.Selection-3.3.csproj">
+      <Project>{2C36CD4F-E5F5-43A4-801A-201EA895FE17}</Project>
+      <Name>HeuristicLab.Selection-3.3</Name>
+    </ProjectReference>
+    <Reference Include="System" />
+    <Reference Include="System.Core">
+      <RequiredTargetFramework>3.5</RequiredTargetFramework>
+    </Reference>
+    <Reference Include="System.Data" />
+    <Reference Include="System.Xml" />
+  </ItemGroup>
+  <ItemGroup>
+    <None Include="HeuristicLab.snk" />
+    <None Include="HeuristicLabAlgorithmsNSGA2Plugin.cs.frame" />
+    <Compile Include="HeuristicLabAlgorithmsNSGA2Plugin.cs" />
+    <Compile Include="NSGA2.cs" />
+    <Compile Include="NSGA2MainLoop.cs" />
+    <Compile Include="Properties\AssemblyInfo.cs" />
+    <None Include="Properties\AssemblyInfo.frame" />
+  </ItemGroup>
+  <ItemGroup>
+    <BootstrapperPackage Include="Microsoft.Net.Client.3.5">
+      <Visible>False</Visible>
+      <ProductName>.NET Framework 3.5 SP1 Client Profile</ProductName>
+      <Install>false</Install>
+    </BootstrapperPackage>
+    <BootstrapperPackage Include="Microsoft.Net.Framework.3.5.SP1">
+      <Visible>False</Visible>
+      <ProductName>.NET Framework 3.5 SP1</ProductName>
+      <Install>true</Install>
+    </BootstrapperPackage>
+    <BootstrapperPackage Include="Microsoft.Windows.Installer.3.1">
+      <Visible>False</Visible>
+      <ProductName>Windows Installer 3.1</ProductName>
+      <Install>true</Install>
+    </BootstrapperPackage>
+  </ItemGroup>
+  <Import Project="$(MSBuildToolsPath)\Microsoft.CSharp.targets" />
+  <!-- To modify your build process, add your task inside one of the targets below and uncomment it. 
+       Other similar extension points exist, see Microsoft.Common.targets.
+  <Target Name="BeforeBuild">
+  </Target>
+  <Target Name="AfterBuild">
+  </Target>
+  -->
+  <PropertyGroup>
+    <PreBuildEvent>set Path=%25Path%25;$(ProjectDir);$(SolutionDir)
+set ProjectDir=$(ProjectDir)
+set SolutionDir=$(SolutionDir)
+set Outdir=$(Outdir)
+
+call PreBuildEvent.cmd
+SubWCRev "%25ProjectDir%25\" "%25ProjectDir%25\HeuristicLabAlgorithmsNSGA2Plugin.cs.frame" "%25ProjectDir%25\HeuristicLabAlgorithmsNSGA2Plugin.cs"</PreBuildEvent>
+  </PropertyGroup>
+  <PropertyGroup>
+    <PostBuildEvent>
+    </PostBuildEvent>
+  </PropertyGroup>
+</Project>
Index: /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/HeuristicLabAlgorithmsNSGA2Plugin.cs.frame
===================================================================
--- /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/HeuristicLabAlgorithmsNSGA2Plugin.cs.frame	(revision 5143)
+++ /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/HeuristicLabAlgorithmsNSGA2Plugin.cs.frame	(revision 5143)
@@ -0,0 +1,41 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using HeuristicLab.PluginInfrastructure;
+
+namespace HeuristicLab.Algorithms.NSGA2 {
+  [Plugin("HeuristicLab.Algorithms.NSGA2", "Provides the non-dominated sorting genetic algorithm (NSGA-II) as described in Deb et al. 2002. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), pp. 182-197.", "3.3.2.$WCREV$")]
+  [PluginFile("HeuristicLab.Algorithms.NSGA2-3.3.dll", PluginFileType.Assembly)]
+  [PluginDependency("HeuristicLab.Analysis", "3.3")]
+  [PluginDependency("HeuristicLab.Collections", "3.3")]
+  [PluginDependency("HeuristicLab.Common", "3.3")]
+  [PluginDependency("HeuristicLab.Core", "3.3")]
+  [PluginDependency("HeuristicLab.Data", "3.3")]
+  [PluginDependency("HeuristicLab.Operators", "3.3")]
+  [PluginDependency("HeuristicLab.Optimization", "3.3")]
+  [PluginDependency("HeuristicLab.Optimization.Operators", "3.3")]
+  [PluginDependency("HeuristicLab.Parameters", "3.3")]
+  [PluginDependency("HeuristicLab.Persistence", "3.3")]
+  [PluginDependency("HeuristicLab.Random", "3.3")]
+  [PluginDependency("HeuristicLab.Selection", "3.3")]
+  public class HeuristicLabAlgorithmsNSGA2Plugin : PluginBase {
+  }
+}
Index: /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/NSGA2.cs
===================================================================
--- /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/NSGA2.cs	(revision 5143)
+++ /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/NSGA2.cs	(revision 5143)
@@ -0,0 +1,384 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Linq;
+using HeuristicLab.Analysis;
+using HeuristicLab.Common;
+using HeuristicLab.Core;
+using HeuristicLab.Data;
+using HeuristicLab.Optimization;
+using HeuristicLab.Optimization.Operators;
+using HeuristicLab.Parameters;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.PluginInfrastructure;
+using HeuristicLab.Random;
+
+namespace HeuristicLab.Algorithms.NSGA2 {
+  /// <summary>
+  /// The Nondominated Sorting Genetic Algorithm II was introduced in Deb et al. 2002. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), pp. 182-197.
+  /// </summary>
+  [Item("NSGA-II", "The Nondominated Sorting Genetic Algorithm II was introduced in Deb et al. 2002. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), pp. 182-197.")]
+  [Creatable("Algorithms")]
+  [StorableClass]
+  public class NSGA2 : EngineAlgorithm, IStorableContent {
+    #region Problem Properties
+    public override Type ProblemType {
+      get { return typeof(IMultiObjectiveProblem); }
+    }
+    public new IMultiObjectiveProblem Problem {
+      get { return (IMultiObjectiveProblem)base.Problem; }
+      set { base.Problem = value; }
+    }
+    #endregion
+
+    #region Parameter Properties
+    private ValueParameter<IntValue> SeedParameter {
+      get { return (ValueParameter<IntValue>)Parameters["Seed"]; }
+    }
+    private ValueParameter<BoolValue> SetSeedRandomlyParameter {
+      get { return (ValueParameter<BoolValue>)Parameters["SetSeedRandomly"]; }
+    }
+    private ValueParameter<IntValue> PopulationSizeParameter {
+      get { return (ValueParameter<IntValue>)Parameters["PopulationSize"]; }
+    }
+    private ConstrainedValueParameter<ISelector> SelectorParameter {
+      get { return (ConstrainedValueParameter<ISelector>)Parameters["Selector"]; }
+    }
+    private ValueParameter<PercentValue> CrossoverProbabilityParameter {
+      get { return (ValueParameter<PercentValue>)Parameters["CrossoverProbability"]; }
+    }
+    private ConstrainedValueParameter<ICrossover> CrossoverParameter {
+      get { return (ConstrainedValueParameter<ICrossover>)Parameters["Crossover"]; }
+    }
+    private ValueParameter<PercentValue> MutationProbabilityParameter {
+      get { return (ValueParameter<PercentValue>)Parameters["MutationProbability"]; }
+    }
+    private OptionalConstrainedValueParameter<IManipulator> MutatorParameter {
+      get { return (OptionalConstrainedValueParameter<IManipulator>)Parameters["Mutator"]; }
+    }
+    private ValueParameter<MultiAnalyzer> AnalyzerParameter {
+      get { return (ValueParameter<MultiAnalyzer>)Parameters["Analyzer"]; }
+    }
+    private ValueParameter<IntValue> MaximumGenerationsParameter {
+      get { return (ValueParameter<IntValue>)Parameters["MaximumGenerations"]; }
+    }
+    private ValueParameter<IntValue> SelectedParentsParameter {
+      get { return (ValueParameter<IntValue>)Parameters["SelectedParents"]; }
+    }
+    #endregion
+
+    #region Properties
+    public IntValue Seed {
+      get { return SeedParameter.Value; }
+      set { SeedParameter.Value = value; }
+    }
+    public BoolValue SetSeedRandomly {
+      get { return SetSeedRandomlyParameter.Value; }
+      set { SetSeedRandomlyParameter.Value = value; }
+    }
+    public IntValue PopulationSize {
+      get { return PopulationSizeParameter.Value; }
+      set { PopulationSizeParameter.Value = value; }
+    }
+    public ISelector Selector {
+      get { return SelectorParameter.Value; }
+      set { SelectorParameter.Value = value; }
+    }
+    public PercentValue CrossoverProbability {
+      get { return CrossoverProbabilityParameter.Value; }
+      set { CrossoverProbabilityParameter.Value = value; }
+    }
+    public ICrossover Crossover {
+      get { return CrossoverParameter.Value; }
+      set { CrossoverParameter.Value = value; }
+    }
+    public PercentValue MutationProbability {
+      get { return MutationProbabilityParameter.Value; }
+      set { MutationProbabilityParameter.Value = value; }
+    }
+    public IManipulator Mutator {
+      get { return MutatorParameter.Value; }
+      set { MutatorParameter.Value = value; }
+    }
+    public MultiAnalyzer Analyzer {
+      get { return AnalyzerParameter.Value; }
+      set { AnalyzerParameter.Value = value; }
+    }
+    public IntValue MaximumGenerations {
+      get { return MaximumGenerationsParameter.Value; }
+      set { MaximumGenerationsParameter.Value = value; }
+    }
+    public IntValue SelectedParents {
+      get { return SelectedParentsParameter.Value; }
+      set { SelectedParentsParameter.Value = value; }
+    }
+    private RandomCreator RandomCreator {
+      get { return (RandomCreator)OperatorGraph.InitialOperator; }
+    }
+    private SolutionsCreator SolutionsCreator {
+      get { return (SolutionsCreator)RandomCreator.Successor; }
+    }
+    private RankAndCrowdingSorter RankAndCrowdingSorter {
+      get { return (RankAndCrowdingSorter)SolutionsCreator.Successor; }
+    }
+    private NSGA2MainLoop MainLoop {
+      get { return (NSGA2MainLoop)RankAndCrowdingSorter.Successor; }
+    }
+    #endregion
+
+    [Storable]
+    private RankBasedParetoFrontAnalyzer paretoFrontAnalyzer;
+
+    [StorableConstructor]
+    protected NSGA2(bool deserializing) : base(deserializing) { }
+    protected NSGA2(NSGA2 original, Cloner cloner) : base (original, cloner) {
+      paretoFrontAnalyzer = (RankBasedParetoFrontAnalyzer)cloner.Clone(original.paretoFrontAnalyzer);
+      AttachEventHandlers();
+    }
+    public NSGA2() {
+      Parameters.Add(new ValueParameter<IntValue>("Seed", "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
+      Parameters.Add(new ValueParameter<BoolValue>("SetSeedRandomly", "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
+      Parameters.Add(new ValueParameter<IntValue>("PopulationSize", "The size of the population of solutions.", new IntValue(100)));
+      Parameters.Add(new ConstrainedValueParameter<ISelector>("Selector", "The operator used to select solutions for reproduction."));
+      Parameters.Add(new ValueParameter<PercentValue>("CrossoverProbability", "The probability that the crossover operator is applied on two parents.", new PercentValue(0.9)));
+      Parameters.Add(new ConstrainedValueParameter<ICrossover>("Crossover", "The operator used to cross solutions."));
+      Parameters.Add(new ValueParameter<PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution.", new PercentValue(0.05)));
+      Parameters.Add(new OptionalConstrainedValueParameter<IManipulator>("Mutator", "The operator used to mutate solutions."));
+      Parameters.Add(new ValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze each generation.", new MultiAnalyzer()));
+      Parameters.Add(new ValueParameter<IntValue>("MaximumGenerations", "The maximum number of generations which should be processed.", new IntValue(1000)));
+      Parameters.Add(new ValueParameter<IntValue>("SelectedParents", "Each two parents form a new child, typically this value should be twice the population size, but because the NSGA-II is maximally elitist it can be any multiple of 2 greater than 0.", new IntValue(200)));
+
+      RandomCreator randomCreator = new RandomCreator();
+      SolutionsCreator solutionsCreator = new SolutionsCreator();
+      RankAndCrowdingSorter rankAndCrowdingSorter = new RankAndCrowdingSorter();
+      NSGA2MainLoop mainLoop = new NSGA2MainLoop();
+      
+      OperatorGraph.InitialOperator = randomCreator;
+
+      randomCreator.RandomParameter.ActualName = "Random";
+      randomCreator.SeedParameter.ActualName = SeedParameter.Name;
+      randomCreator.SeedParameter.Value = null;
+      randomCreator.SetSeedRandomlyParameter.ActualName = SetSeedRandomlyParameter.Name;
+      randomCreator.SetSeedRandomlyParameter.Value = null;
+      randomCreator.Successor = solutionsCreator;
+
+      solutionsCreator.NumberOfSolutionsParameter.ActualName = PopulationSizeParameter.Name;
+      solutionsCreator.Successor = rankAndCrowdingSorter;
+
+      rankAndCrowdingSorter.CrowdingDistanceParameter.ActualName = "CrowdingDistance";
+      rankAndCrowdingSorter.RankParameter.ActualName = "Rank";
+      rankAndCrowdingSorter.Successor = mainLoop;
+
+      mainLoop.PopulationSizeParameter.ActualName = PopulationSizeParameter.Name;
+      mainLoop.SelectorParameter.ActualName = SelectorParameter.Name;
+      mainLoop.CrossoverParameter.ActualName = CrossoverParameter.Name;
+      mainLoop.CrossoverProbabilityParameter.ActualName = CrossoverProbabilityParameter.Name;
+      mainLoop.MaximumGenerationsParameter.ActualName = MaximumGenerationsParameter.Name;
+      mainLoop.MutatorParameter.ActualName = MutatorParameter.Name;
+      mainLoop.MutationProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
+      mainLoop.RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
+      mainLoop.AnalyzerParameter.ActualName = AnalyzerParameter.Name;
+      mainLoop.ResultsParameter.ActualName = "Results";
+
+      foreach (ISelector selector in ApplicationManager.Manager.GetInstances<ISelector>().Where(x => !(x is ISingleObjectiveSelector)).OrderBy(x => x.Name))
+        SelectorParameter.ValidValues.Add(selector);
+      ISelector tournamentSelector = SelectorParameter.ValidValues.FirstOrDefault(x => x.GetType().Name.Equals("CrowdedTournamentSelector"));
+      if (tournamentSelector != null) SelectorParameter.Value = tournamentSelector;
+
+      ParameterizeSelectors();
+
+      paretoFrontAnalyzer = new RankBasedParetoFrontAnalyzer();
+      paretoFrontAnalyzer.RankParameter.ActualName = "Rank";
+      paretoFrontAnalyzer.RankParameter.Depth = 1;
+      paretoFrontAnalyzer.ResultsParameter.ActualName = "Results";
+      ParameterizeAnalyzers();
+      UpdateAnalyzers();
+
+      AttachEventHandlers();
+    }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new NSGA2(this, cloner);      
+    }
+
+    #region Events
+    protected override void OnProblemChanged() {
+      ParameterizeStochasticOperator(Problem.SolutionCreator);
+      ParameterizeStochasticOperator(Problem.Evaluator);
+      foreach (IOperator op in Problem.Operators) ParameterizeStochasticOperator(op);
+      ParameterizeSolutionsCreator();
+      ParameterizeRankAndCrowdingSorter();
+      ParameterizeMainLoop();
+      ParameterizeSelectors();
+      ParameterizeAnalyzers();
+      ParameterizeIterationBasedOperators();
+      UpdateCrossovers();
+      UpdateMutators();
+      UpdateAnalyzers();
+      Problem.Evaluator.QualitiesParameter.ActualNameChanged += new EventHandler(Evaluator_QualitiesParameter_ActualNameChanged);
+      base.OnProblemChanged();
+    }
+    protected override void Problem_SolutionCreatorChanged(object sender, EventArgs e) {
+      ParameterizeStochasticOperator(Problem.SolutionCreator);
+      ParameterizeSolutionsCreator();
+      base.Problem_SolutionCreatorChanged(sender, e);
+    }
+    protected override void Problem_EvaluatorChanged(object sender, EventArgs e) {
+      ParameterizeStochasticOperator(Problem.Evaluator);
+      ParameterizeSolutionsCreator();
+      ParameterizeRankAndCrowdingSorter();
+      ParameterizeMainLoop();
+      ParameterizeSelectors();
+      ParameterizeAnalyzers();
+      Problem.Evaluator.QualitiesParameter.ActualNameChanged += new EventHandler(Evaluator_QualitiesParameter_ActualNameChanged);
+      base.Problem_EvaluatorChanged(sender, e);
+    }
+    protected override void Problem_OperatorsChanged(object sender, EventArgs e) {
+      foreach (IOperator op in Problem.Operators) ParameterizeStochasticOperator(op);
+      ParameterizeIterationBasedOperators();
+      UpdateCrossovers();
+      UpdateMutators();
+      UpdateAnalyzers();
+      base.Problem_OperatorsChanged(sender, e);
+    }
+    protected override void Problem_Reset(object sender, EventArgs e) {
+      base.Problem_Reset(sender, e);
+    }
+    private void PopulationSizeParameter_ValueChanged(object sender, EventArgs e) {
+      PopulationSize.ValueChanged += new EventHandler(PopulationSize_ValueChanged);
+      ParameterizeSelectors();
+    }
+    private void PopulationSize_ValueChanged(object sender, EventArgs e) {
+      ParameterizeSelectors();
+    }
+    private void Evaluator_QualitiesParameter_ActualNameChanged(object sender, EventArgs e) {
+      ParameterizeRankAndCrowdingSorter();
+      ParameterizeMainLoop();
+      ParameterizeSelectors();
+      ParameterizeAnalyzers();
+    }
+    private void SelectedParentsParameter_ValueChanged(object sender, EventArgs e) {
+      SelectedParents.ValueChanged += new EventHandler(SelectedParents_ValueChanged);
+      SelectedParents_ValueChanged(null, EventArgs.Empty);
+    }
+    private void SelectedParents_ValueChanged(object sender, EventArgs e) {
+      if (SelectedParents.Value < 2) SelectedParents.Value = 2;
+      else if (SelectedParents.Value % 2 != 0) {
+        SelectedParents.Value = SelectedParents.Value + 1;
+      }
+    }
+    #endregion
+
+    #region Helpers
+    [StorableHook(HookType.AfterDeserialization)]
+    private void AttachEventHandlers() {
+      PopulationSizeParameter.ValueChanged += new EventHandler(PopulationSizeParameter_ValueChanged);
+      PopulationSize.ValueChanged += new EventHandler(PopulationSize_ValueChanged);
+      SelectedParentsParameter.ValueChanged += new EventHandler(SelectedParentsParameter_ValueChanged);
+      SelectedParents.ValueChanged += new EventHandler(SelectedParents_ValueChanged);
+      if (Problem != null) {
+        Problem.Evaluator.QualitiesParameter.ActualNameChanged += new EventHandler(Evaluator_QualitiesParameter_ActualNameChanged);
+      }
+    }
+    private void ParameterizeSolutionsCreator() {
+      SolutionsCreator.EvaluatorParameter.ActualName = Problem.EvaluatorParameter.Name;
+      SolutionsCreator.SolutionCreatorParameter.ActualName = Problem.SolutionCreatorParameter.Name;
+    }
+    private void ParameterizeRankAndCrowdingSorter() {
+      RankAndCrowdingSorter.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name;
+      RankAndCrowdingSorter.QualitiesParameter.ActualName = Problem.Evaluator.QualitiesParameter.ActualName;
+    }
+    private void ParameterizeMainLoop() {
+      MainLoop.EvaluatorParameter.ActualName = Problem.EvaluatorParameter.Name;
+      MainLoop.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name;
+      MainLoop.QualitiesParameter.ActualName = Problem.Evaluator.QualitiesParameter.ActualName;
+    }
+    private void ParameterizeStochasticOperator(IOperator op) {
+      if (op is IStochasticOperator)
+        ((IStochasticOperator)op).RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
+    }
+    private void ParameterizeSelectors() {
+      foreach (ISelector selector in SelectorParameter.ValidValues) {
+        selector.CopySelected = new BoolValue(true);
+        selector.NumberOfSelectedSubScopesParameter.ActualName = SelectedParentsParameter.Name;
+        ParameterizeStochasticOperator(selector);
+      }
+      if (Problem != null) {
+        foreach (IMultiObjectiveSelector selector in SelectorParameter.ValidValues.OfType<IMultiObjectiveSelector>()) {
+          selector.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name;
+          selector.QualitiesParameter.ActualName = Problem.Evaluator.QualitiesParameter.ActualName;
+        }
+      }
+    }
+    private void ParameterizeAnalyzers() {
+      if (Problem != null) {
+        paretoFrontAnalyzer.QualitiesParameter.ActualName = Problem.Evaluator.QualitiesParameter.ActualName;
+        paretoFrontAnalyzer.QualitiesParameter.Depth = 1;
+      }
+    }
+    private void ParameterizeIterationBasedOperators() {
+      if (Problem != null) {
+        foreach (IIterationBasedOperator op in Problem.Operators.OfType<IIterationBasedOperator>()) {
+          op.IterationsParameter.ActualName = "Generations";
+          op.MaximumIterationsParameter.ActualName = "MaximumGenerations";
+        }
+      }
+    }
+    private void UpdateCrossovers() {
+      ICrossover oldCrossover = CrossoverParameter.Value;
+      CrossoverParameter.ValidValues.Clear();
+      foreach (ICrossover crossover in Problem.Operators.OfType<ICrossover>().OrderBy(x => x.Name))
+        CrossoverParameter.ValidValues.Add(crossover);
+      if (oldCrossover != null) {
+        ICrossover crossover = CrossoverParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldCrossover.GetType());
+        if (crossover != null) CrossoverParameter.Value = crossover;
+      }
+    }
+    private void UpdateMutators() {
+      IManipulator oldMutator = MutatorParameter.Value;
+      MutatorParameter.ValidValues.Clear();
+      foreach (IManipulator mutator in Problem.Operators.OfType<IManipulator>().OrderBy(x => x.Name))
+        MutatorParameter.ValidValues.Add(mutator);
+      if (oldMutator != null) {
+        IManipulator mutator = MutatorParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldMutator.GetType());
+        if (mutator != null) MutatorParameter.Value = mutator;
+      }
+    }
+    private void UpdateAnalyzers() {
+      Analyzer.Operators.Clear();
+      if (Problem != null) {
+        foreach (IAnalyzer analyzer in Problem.Operators.OfType<IAnalyzer>()) {
+          foreach (IScopeTreeLookupParameter param in analyzer.Parameters.OfType<IScopeTreeLookupParameter>())
+            param.Depth = 1;
+          Analyzer.Operators.Add(analyzer);
+        }
+      }
+      Analyzer.Operators.Add(paretoFrontAnalyzer);
+    }
+    #endregion
+
+    public string Filename {
+      get;
+      set;
+    }
+  }
+}
Index: /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/NSGA2MainLoop.cs
===================================================================
--- /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/NSGA2MainLoop.cs	(revision 5143)
+++ /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/NSGA2MainLoop.cs	(revision 5143)
@@ -0,0 +1,227 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using HeuristicLab.Core;
+using HeuristicLab.Data;
+using HeuristicLab.Operators;
+using HeuristicLab.Optimization.Operators;
+using HeuristicLab.Parameters;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Selection;
+using HeuristicLab.Common;
+
+namespace HeuristicLab.Algorithms.NSGA2 {
+  /// <summary>
+  /// An operator that represents the mainloop of the NSGA-II
+  /// </summary>
+  [Item("NSGA2MainLoop", "An operator which represents the main loop of the NSGA-II algorithm.")]
+  [StorableClass]
+  public class NSGA2MainLoop : AlgorithmOperator {
+    #region Parameter properties
+    public ValueLookupParameter<IRandom> RandomParameter {
+      get { return (ValueLookupParameter<IRandom>)Parameters["Random"]; }
+    }
+    public ValueLookupParameter<BoolArray> MaximizationParameter {
+      get { return (ValueLookupParameter<BoolArray>)Parameters["Maximization"]; }
+    }
+    public ScopeTreeLookupParameter<DoubleArray> QualitiesParameter {
+      get { return (ScopeTreeLookupParameter<DoubleArray>)Parameters["Qualities"]; }
+    }
+    public ValueLookupParameter<IntValue> PopulationSizeParameter {
+      get { return (ValueLookupParameter<IntValue>)Parameters["PopulationSize"]; }
+    }
+    public ValueLookupParameter<IOperator> SelectorParameter {
+      get { return (ValueLookupParameter<IOperator>)Parameters["Selector"]; }
+    }
+    public ValueLookupParameter<PercentValue> CrossoverProbabilityParameter {
+      get { return (ValueLookupParameter<PercentValue>)Parameters["CrossoverProbability"]; }
+    }
+    public ValueLookupParameter<IOperator> CrossoverParameter {
+      get { return (ValueLookupParameter<IOperator>)Parameters["Crossover"]; }
+    }
+    public ValueLookupParameter<PercentValue> MutationProbabilityParameter {
+      get { return (ValueLookupParameter<PercentValue>)Parameters["MutationProbability"]; }
+    }
+    public ValueLookupParameter<IOperator> MutatorParameter {
+      get { return (ValueLookupParameter<IOperator>)Parameters["Mutator"]; }
+    }
+    public ValueLookupParameter<IOperator> EvaluatorParameter {
+      get { return (ValueLookupParameter<IOperator>)Parameters["Evaluator"]; }
+    }
+    public ValueLookupParameter<IntValue> MaximumGenerationsParameter {
+      get { return (ValueLookupParameter<IntValue>)Parameters["MaximumGenerations"]; }
+    }
+    public ValueLookupParameter<VariableCollection> ResultsParameter {
+      get { return (ValueLookupParameter<VariableCollection>)Parameters["Results"]; }
+    }
+    public ValueLookupParameter<IOperator> AnalyzerParameter {
+      get { return (ValueLookupParameter<IOperator>)Parameters["Analyzer"]; }
+    }
+    #endregion
+
+    [StorableConstructor]
+    protected NSGA2MainLoop(bool deserializing) : base(deserializing) { }
+    protected NSGA2MainLoop(NSGA2MainLoop original, Cloner cloner) : base(original, cloner) { }
+    public NSGA2MainLoop()
+      : base() {
+      Initialize();
+    }
+
+    private void Initialize() {
+      #region Create parameters
+      Parameters.Add(new ValueLookupParameter<IRandom>("Random", "A pseudo random number generator."));
+      Parameters.Add(new ValueLookupParameter<BoolArray>("Maximization", "True if an objective should be maximized, or false if it should be minimized."));
+      Parameters.Add(new ScopeTreeLookupParameter<DoubleArray>("Qualities", "The vector of quality values."));
+      Parameters.Add(new ValueLookupParameter<IntValue>("PopulationSize", "The population size."));
+      Parameters.Add(new ValueLookupParameter<IOperator>("Selector", "The operator used to select solutions for reproduction."));
+      Parameters.Add(new ValueLookupParameter<PercentValue>("CrossoverProbability", "The probability that the crossover operator is applied on a solution."));
+      Parameters.Add(new ValueLookupParameter<IOperator>("Crossover", "The operator used to cross solutions."));
+      Parameters.Add(new ValueLookupParameter<PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution."));
+      Parameters.Add(new ValueLookupParameter<IOperator>("Mutator", "The operator used to mutate solutions."));
+      Parameters.Add(new ValueLookupParameter<IOperator>("Evaluator", "The operator used to evaluate solutions."));
+      Parameters.Add(new ValueLookupParameter<IntValue>("MaximumGenerations", "The maximum number of generations which should be processed."));
+      Parameters.Add(new ValueLookupParameter<VariableCollection>("Results", "The variable collection where results should be stored."));
+      Parameters.Add(new ValueLookupParameter<IOperator>("Analyzer", "The operator used to analyze each generation."));
+      #endregion
+
+      #region Create operators
+      VariableCreator variableCreator = new VariableCreator();
+      ResultsCollector resultsCollector1 = new ResultsCollector();
+      Placeholder analyzer1 = new Placeholder();
+      Placeholder selector = new Placeholder();
+      SubScopesProcessor subScopesProcessor1 = new SubScopesProcessor();
+      ChildrenCreator childrenCreator = new ChildrenCreator();
+      UniformSubScopesProcessor uniformSubScopesProcessor = new UniformSubScopesProcessor();
+      StochasticBranch crossoverStochasticBranch = new StochasticBranch();
+      Placeholder crossover = new Placeholder();
+      ParentCopyCrossover noCrossover = new ParentCopyCrossover();
+      StochasticBranch mutationStochasticBranch = new StochasticBranch();
+      Placeholder mutator = new Placeholder();
+      Placeholder evaluator = new Placeholder();
+      SubScopesRemover subScopesRemover = new SubScopesRemover();
+      MergingReducer mergingReducer = new MergingReducer();
+      RankAndCrowdingSorter rankAndCrowdingSorter = new RankAndCrowdingSorter();
+      LeftSelector leftSelector = new LeftSelector();
+      RightReducer rightReducer = new RightReducer();
+      IntCounter intCounter = new IntCounter();
+      Comparator comparator = new Comparator();
+      ResultsCollector resultsCollector2 = new ResultsCollector();
+      Placeholder analyzer2 = new Placeholder();
+      ConditionalBranch conditionalBranch = new ConditionalBranch();
+
+      variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("Generations", new IntValue(0)));
+
+      resultsCollector1.CollectedValues.Add(new LookupParameter<IntValue>("Generations"));
+      resultsCollector1.ResultsParameter.ActualName = ResultsParameter.Name;
+
+      analyzer1.Name = "Analyzer";
+      analyzer1.OperatorParameter.ActualName = AnalyzerParameter.Name;
+
+      selector.Name = "Selector";
+      selector.OperatorParameter.ActualName = SelectorParameter.Name;
+
+      childrenCreator.ParentsPerChild = new IntValue(2);
+
+      crossoverStochasticBranch.ProbabilityParameter.ActualName = CrossoverProbabilityParameter.Name;
+      crossoverStochasticBranch.RandomParameter.ActualName = RandomParameter.Name;
+
+      crossover.Name = "Crossover";
+      crossover.OperatorParameter.ActualName = CrossoverParameter.Name;
+
+      noCrossover.Name = "Clone parent";
+      noCrossover.RandomParameter.ActualName = RandomParameter.Name;
+
+      mutationStochasticBranch.ProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
+      mutationStochasticBranch.RandomParameter.ActualName = RandomParameter.Name;
+
+      mutator.Name = "Mutator";
+      mutator.OperatorParameter.ActualName = MutatorParameter.Name;
+
+      evaluator.Name = "Evaluator";
+      evaluator.OperatorParameter.ActualName = EvaluatorParameter.Name;
+
+      subScopesRemover.RemoveAllSubScopes = true;
+
+      rankAndCrowdingSorter.CrowdingDistanceParameter.ActualName = "CrowdingDistance";
+      rankAndCrowdingSorter.RankParameter.ActualName = "Rank";
+
+      leftSelector.CopySelected = new BoolValue(false);
+      leftSelector.NumberOfSelectedSubScopesParameter.ActualName = PopulationSizeParameter.Name;
+
+      intCounter.Increment = new IntValue(1);
+      intCounter.ValueParameter.ActualName = "Generations";
+
+      comparator.Comparison = new Comparison(ComparisonType.GreaterOrEqual);
+      comparator.LeftSideParameter.ActualName = "Generations";
+      comparator.ResultParameter.ActualName = "Terminate";
+      comparator.RightSideParameter.ActualName = MaximumGenerationsParameter.Name;
+
+      resultsCollector2.CollectedValues.Add(new LookupParameter<IntValue>("Generations"));
+      resultsCollector2.ResultsParameter.ActualName = ResultsParameter.Name;
+
+      analyzer2.Name = "Analyzer";
+      analyzer2.OperatorParameter.ActualName = "Analyzer";
+
+      conditionalBranch.ConditionParameter.ActualName = "Terminate";
+      #endregion
+
+      #region Create operator graph
+      OperatorGraph.InitialOperator = variableCreator;
+      variableCreator.Successor = resultsCollector1;
+      resultsCollector1.Successor = analyzer1;
+      analyzer1.Successor = selector;
+      selector.Successor = subScopesProcessor1;
+      subScopesProcessor1.Operators.Add(new EmptyOperator());
+      subScopesProcessor1.Operators.Add(childrenCreator);
+      subScopesProcessor1.Successor = mergingReducer;
+      childrenCreator.Successor = uniformSubScopesProcessor;
+      uniformSubScopesProcessor.Operator = crossoverStochasticBranch;
+      uniformSubScopesProcessor.Successor = null;
+      crossoverStochasticBranch.FirstBranch = crossover;
+      crossoverStochasticBranch.SecondBranch = noCrossover;
+      crossoverStochasticBranch.Successor = mutationStochasticBranch;
+      crossover.Successor = null;
+      noCrossover.Successor = null;
+      mutationStochasticBranch.FirstBranch = mutator;
+      mutationStochasticBranch.SecondBranch = null;
+      mutationStochasticBranch.Successor = evaluator;
+      mutator.Successor = null;
+      evaluator.Successor = subScopesRemover;
+      subScopesRemover.Successor = null;
+      mergingReducer.Successor = rankAndCrowdingSorter;
+      rankAndCrowdingSorter.Successor = leftSelector;
+      leftSelector.Successor = rightReducer;
+      rightReducer.Successor = intCounter;
+      intCounter.Successor = comparator;
+      comparator.Successor = resultsCollector2;
+      resultsCollector2.Successor = analyzer2;
+      analyzer2.Successor = conditionalBranch;
+      conditionalBranch.FalseBranch = selector;
+      conditionalBranch.TrueBranch = null;
+      conditionalBranch.Successor = null;
+      #endregion
+    }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new NSGA2MainLoop(this, cloner);
+    }
+  }
+}
Index: /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/Properties/AssemblyInfo.frame
===================================================================
--- /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/Properties/AssemblyInfo.frame	(revision 5143)
+++ /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/Properties/AssemblyInfo.frame	(revision 5143)
@@ -0,0 +1,36 @@
+﻿using System.Reflection;
+using System.Runtime.CompilerServices;
+using System.Runtime.InteropServices;
+
+// General Information about an assembly is controlled through the following 
+// set of attributes. Change these attribute values to modify the information
+// associated with an assembly.
+[assembly: AssemblyTitle("HeuristicLab.Algorithms.NSGA2")]
+[assembly: AssemblyDescription("")]
+[assembly: AssemblyConfiguration("")]
+[assembly: AssemblyCompany("HEAL")]
+[assembly: AssemblyProduct("HeuristicLab")]
+[assembly: AssemblyCopyright("(c) 2002-2010 HEAL")]
+[assembly: AssemblyTrademark("")]
+[assembly: AssemblyCulture("")]
+
+// Setting ComVisible to false makes the types in this assembly not visible 
+// to COM components.  If you need to access a type in this assembly from 
+// COM, set the ComVisible attribute to true on that type.
+[assembly: ComVisible(false)]
+
+// The following GUID is for the ID of the typelib if this project is exposed to COM
+[assembly: Guid("7d0fdb00-8511-45a2-ae51-c830b0c9b548")]
+
+// Version information for an assembly consists of the following four values:
+//
+//      Major Version
+//      Minor Version 
+//      Build Number
+//      Revision
+//
+// You can specify all the values or you can default the Build and Revision Numbers 
+// by using the '*' as shown below:
+// [assembly: AssemblyVersion("1.0.*")]
+[assembly: AssemblyVersion("3.3.0.0")]
+[assembly: AssemblyFileVersion("3.3.2.$WCREV$")]
Index: /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/RankAndCrowdingSorter.cs
===================================================================
--- /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/RankAndCrowdingSorter.cs	(revision 5143)
+++ /trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/RankAndCrowdingSorter.cs	(revision 5143)
@@ -0,0 +1,84 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using HeuristicLab.Core;
+using HeuristicLab.Data;
+using HeuristicLab.Operators;
+using HeuristicLab.Parameters;
+using HeuristicLab.Selection;
+using HeuristicLab.Common;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+
+namespace HeuristicLab.Algorithms.NSGA2 {
+  public class RankAndCrowdingSorter : AlgorithmOperator {
+    public ValueLookupParameter<BoolArray> MaximizationParameter {
+      get { return (ValueLookupParameter<BoolArray>)Parameters["Maximization"]; }
+    }
+    public ScopeTreeLookupParameter<DoubleArray> QualitiesParameter {
+      get { return (ScopeTreeLookupParameter<DoubleArray>)Parameters["Qualities"]; }
+    }
+    public ScopeTreeLookupParameter<IntValue> RankParameter {
+      get { return (ScopeTreeLookupParameter<IntValue>)Parameters["Rank"]; }
+    }
+    public ScopeTreeLookupParameter<DoubleValue> CrowdingDistanceParameter {
+      get { return (ScopeTreeLookupParameter<DoubleValue>)Parameters["CrowdingDistance"]; }
+    }
+
+    [StorableConstructor]
+    protected RankAndCrowdingSorter(bool deserializing) : base(deserializing) { }
+    protected RankAndCrowdingSorter(RankAndCrowdingSorter original, Cloner cloner) : base(original, cloner) { }
+    public RankAndCrowdingSorter()
+      : base() {
+      Parameters.Add(new ValueLookupParameter<BoolArray>("Maximization", "For each objective a value that is true if that objective should be maximized, or false if it should be minimized."));
+      Parameters.Add(new ScopeTreeLookupParameter<DoubleArray>("Qualities", "The vector of quality values."));
+      Parameters.Add(new ScopeTreeLookupParameter<IntValue>("Rank", "The rank of a solution (to which front it belongs)."));
+      Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("CrowdingDistance", "The crowding distance of a solution in a population."));
+
+      FastNonDominatedSort fastNonDominatedSort = new FastNonDominatedSort();
+      UniformSubScopesProcessor subScopesProcessor = new UniformSubScopesProcessor();
+      CrowdingDistanceAssignment crowdingDistanceAssignment = new CrowdingDistanceAssignment();
+      CrowdedComparisonSorter crowdedComparisonSorter = new CrowdedComparisonSorter();
+      MergingReducer mergingReducer = new MergingReducer();
+
+      fastNonDominatedSort.MaximizationParameter.ActualName = MaximizationParameter.Name;
+      fastNonDominatedSort.QualitiesParameter.ActualName = QualitiesParameter.Name;
+      fastNonDominatedSort.RankParameter.ActualName = RankParameter.Name;
+
+      crowdingDistanceAssignment.CrowdingDistanceParameter.ActualName = CrowdingDistanceParameter.Name;
+      crowdingDistanceAssignment.QualitiesParameter.ActualName = QualitiesParameter.Name;
+
+      crowdedComparisonSorter.CrowdingDistanceParameter.ActualName = CrowdingDistanceParameter.Name;
+      crowdedComparisonSorter.RankParameter.ActualName = RankParameter.Name;
+
+      OperatorGraph.InitialOperator = fastNonDominatedSort;
+      fastNonDominatedSort.Successor = subScopesProcessor;
+      subScopesProcessor.Operator = crowdingDistanceAssignment;
+      crowdingDistanceAssignment.Successor = crowdedComparisonSorter;
+      crowdedComparisonSorter.Successor = null;
+      subScopesProcessor.Successor = mergingReducer;
+      mergingReducer.Successor = null;
+    }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new RankAndCrowdingSorter(this, cloner);
+    }
+  }
+}
Index: /trunk/sources/HeuristicLab.Analysis/3.3/HeuristicLab.Analysis-3.3.csproj
===================================================================
--- /trunk/sources/HeuristicLab.Analysis/3.3/HeuristicLab.Analysis-3.3.csproj	(revision 5142)
+++ /trunk/sources/HeuristicLab.Analysis/3.3/HeuristicLab.Analysis-3.3.csproj	(revision 5143)
@@ -124,4 +124,6 @@
     <Compile Include="DataVisualization\HeatMap.cs" />
     <Compile Include="DataVisualization\HeatMapHistory.cs" />
+    <Compile Include="MultiObjective\RankBasedParetoFrontAnalyzer.cs" />
+    <Compile Include="MultiObjective\ParetoFrontAnalyzer.cs" />
     <Compile Include="PopulationDiversityAnalysis\PopulationDiversityAnalyzer.cs" />
     <Compile Include="QualityAnalysis\BestAverageWorstQualityAnalyzer.cs" />
Index: /trunk/sources/HeuristicLab.Analysis/3.3/MultiObjective/ParetoFrontAnalyzer.cs
===================================================================
--- /trunk/sources/HeuristicLab.Analysis/3.3/MultiObjective/ParetoFrontAnalyzer.cs	(revision 5143)
+++ /trunk/sources/HeuristicLab.Analysis/3.3/MultiObjective/ParetoFrontAnalyzer.cs	(revision 5143)
@@ -0,0 +1,37 @@
+﻿using HeuristicLab.Common;
+using HeuristicLab.Core;
+using HeuristicLab.Data;
+using HeuristicLab.Operators;
+using HeuristicLab.Optimization;
+using HeuristicLab.Parameters;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+
+namespace HeuristicLab.Analysis {
+  [Item("BasicMultiObjectiveSolutionAnalyzer", "Analyzer for multiobjective problems that collects and presents the current pareto front as double matrix as well as the solution scopes that lie on the current front.")]
+  [StorableClass]
+  public abstract class ParetoFrontAnalyzer : SingleSuccessorOperator, IAnalyzer {
+    public IScopeTreeLookupParameter<DoubleArray> QualitiesParameter {
+      get { return (IScopeTreeLookupParameter<DoubleArray>)Parameters["Qualities"]; }
+    }
+    public ILookupParameter<ResultCollection> ResultsParameter {
+      get { return (ILookupParameter<ResultCollection>)Parameters["Results"]; }
+    }
+
+    [StorableConstructor]
+    protected ParetoFrontAnalyzer(bool deserializing) : base(deserializing) { }
+    protected ParetoFrontAnalyzer(ParetoFrontAnalyzer original, Cloner cloner) : base(original, cloner) { }
+    public ParetoFrontAnalyzer() {
+      Parameters.Add(new ScopeTreeLookupParameter<DoubleArray>("Qualities", "The vector of qualities of each solution."));
+      Parameters.Add(new LookupParameter<ResultCollection>("Results", "The result collection to store the front to."));
+    }
+
+    public override IOperation Apply() {
+      ItemArray<DoubleArray> qualities = QualitiesParameter.ActualValue;
+      ResultCollection results = ResultsParameter.ActualValue;
+      Analyze(qualities, results);
+      return base.Apply();
+    }
+
+    protected abstract void Analyze(ItemArray<DoubleArray> qualities, ResultCollection results);
+  }
+}
Index: /trunk/sources/HeuristicLab.Analysis/3.3/MultiObjective/RankBasedParetoFrontAnalyzer.cs
===================================================================
--- /trunk/sources/HeuristicLab.Analysis/3.3/MultiObjective/RankBasedParetoFrontAnalyzer.cs	(revision 5143)
+++ /trunk/sources/HeuristicLab.Analysis/3.3/MultiObjective/RankBasedParetoFrontAnalyzer.cs	(revision 5143)
@@ -0,0 +1,97 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System.Collections.Generic;
+using System.Linq;
+using HeuristicLab.Common;
+using HeuristicLab.Core;
+using HeuristicLab.Data;
+using HeuristicLab.Optimization;
+using HeuristicLab.Parameters;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+
+namespace HeuristicLab.Analysis {
+  [Item("RankBasedParetoFrontAnalyzer", "Uses the rank value that is computed by e.g. the NSGA2's fast non dominated sort operator to collect all solutions and their qualities of front 0 (the current pareto front).")]
+  [StorableClass]
+  public class RankBasedParetoFrontAnalyzer : ParetoFrontAnalyzer {
+    public IScopeTreeLookupParameter<IntValue> RankParameter {
+      get { return (IScopeTreeLookupParameter<IntValue>)Parameters["Rank"]; }
+    }
+
+    [StorableConstructor]
+    protected RankBasedParetoFrontAnalyzer(bool deserializing) : base(deserializing) { }
+    protected RankBasedParetoFrontAnalyzer(RankBasedParetoFrontAnalyzer original, Cloner cloner) : base(original, cloner) { }
+    public RankBasedParetoFrontAnalyzer() {
+      Parameters.Add(new ScopeTreeLookupParameter<IntValue>("Rank", "The rank of solution [0..N] describes to which front it belongs."));
+    }
+
+    protected override void Analyze(ItemArray<DoubleArray> qualities, ResultCollection results) {
+      ItemArray<IntValue> ranks = RankParameter.ActualValue;
+
+      bool populationLevel = RankParameter.Depth == 1;
+
+      int objectives = qualities[0].Length;
+      int frontSize = ranks.Count(x => x.Value == 0);
+      ItemArray<IScope> paretoArchive = null;
+      if (populationLevel) paretoArchive = new ItemArray<IScope>(frontSize);
+
+      DoubleMatrix front = new DoubleMatrix(frontSize, objectives);
+      int counter = 0;
+      for (int i = 0; i < ranks.Length; i++) {
+        if (ranks[i].Value == 0) {
+          for (int k = 0; k < objectives; k++)
+            front[counter, k] = qualities[i][k];
+          if (populationLevel) {
+            paretoArchive[counter] = (IScope)ExecutionContext.Scope.SubScopes[i].Clone();
+          }
+          counter++;
+        }
+      }
+
+      front.RowNames = GetRowNames(front);
+      front.ColumnNames = GetColumnNames(front);
+
+      if (results.ContainsKey("Pareto Front"))
+        results["Pareto Front"].Value = front;
+      else results.Add(new Result("Pareto Front", front));
+
+      if (populationLevel) {
+        if (results.ContainsKey("Pareto Archive"))
+          results["Pareto Archive"].Value = paretoArchive;
+        else results.Add(new Result("Pareto Archive", paretoArchive));
+      }
+    }
+
+    private IEnumerable<string> GetRowNames(DoubleMatrix front) {
+      for (int i = 1; i <= front.Rows; i++)
+        yield return "Solution " + i.ToString();
+    }
+
+    private IEnumerable<string> GetColumnNames(DoubleMatrix front) {
+      for (int i = 1; i <= front.Columns; i++)
+        yield return "Objective " + i.ToString();
+    }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new RankBasedParetoFrontAnalyzer(this, cloner);
+    }
+  }
+}
Index: /trunk/sources/HeuristicLab.Optimization.Operators/3.3/HeuristicLab.Optimization.Operators-3.3.csproj
===================================================================
--- /trunk/sources/HeuristicLab.Optimization.Operators/3.3/HeuristicLab.Optimization.Operators-3.3.csproj	(revision 5142)
+++ /trunk/sources/HeuristicLab.Optimization.Operators/3.3/HeuristicLab.Optimization.Operators-3.3.csproj	(revision 5143)
@@ -103,4 +103,9 @@
   </ItemGroup>
   <ItemGroup>
+    <Compile Include="MultiObjective\CrowdedComparisonSorter.cs" />
+    <Compile Include="MultiObjective\CrowdingDistanceAssignment.cs" />
+    <Compile Include="ParentCopyCrossover.cs" />
+    <Compile Include="MultiObjective\FastNonDominatedSort.cs" />
+    <Compile Include="MultiObjective\RankAndCrowdingSorter.cs" />
     <Compile Include="UserDefinedCrossover.cs" />
     <Compile Include="UserDefinedEvaluator.cs" />
@@ -167,4 +172,8 @@
       <Name>HeuristicLab.PluginInfrastructure-3.3</Name>
     </ProjectReference>
+    <ProjectReference Include="..\..\HeuristicLab.Selection\3.3\HeuristicLab.Selection-3.3.csproj">
+      <Project>{2C36CD4F-E5F5-43A4-801A-201EA895FE17}</Project>
+      <Name>HeuristicLab.Selection-3.3</Name>
+    </ProjectReference>
   </ItemGroup>
   <ItemGroup>
Index: /trunk/sources/HeuristicLab.Optimization.Operators/3.3/HeuristicLabOptimizationOperatorsPlugin.cs.frame
===================================================================
--- /trunk/sources/HeuristicLab.Optimization.Operators/3.3/HeuristicLabOptimizationOperatorsPlugin.cs.frame	(revision 5142)
+++ /trunk/sources/HeuristicLab.Optimization.Operators/3.3/HeuristicLabOptimizationOperatorsPlugin.cs.frame	(revision 5143)
@@ -36,4 +36,5 @@
   [PluginDependency("HeuristicLab.Parameters", "3.3")]
   [PluginDependency("HeuristicLab.Persistence", "3.3")]
+  [PluginDependency("HeuristicLab.Selection", "3.3")]
   public class HeuristicLabOptimizationOperatorsPlugin : PluginBase {
   }
Index: /trunk/sources/HeuristicLab.Optimization.Operators/3.3/MultiObjective/CrowdedComparisonSorter.cs
===================================================================
--- /trunk/sources/HeuristicLab.Optimization.Operators/3.3/MultiObjective/CrowdedComparisonSorter.cs	(revision 5143)
+++ /trunk/sources/HeuristicLab.Optimization.Operators/3.3/MultiObjective/CrowdedComparisonSorter.cs	(revision 5143)
@@ -0,0 +1,99 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using HeuristicLab.Common;
+using HeuristicLab.Core;
+using HeuristicLab.Data;
+using HeuristicLab.Operators;
+using HeuristicLab.Parameters;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+
+namespace HeuristicLab.Optimization.Operators {
+  /// <summary>
+  /// CrowdedComparisonSorter as described in: Deb, Pratap, Agrawal and Meyarivan, "A Fast and Elitist Multiobjective
+  /// Genetic Algorithm: NSGA-II", IEEE Transactions On Evolutionary Computation, Vol. 6, No. 2, April 2002
+  /// </summary>
+  [Item("CrowdedComparisonSorter", @"CrowdedComparisonSorter as described in: Deb, Pratap, Agrawal and Meyarivan, ""A Fast and Elitist Multiobjective
+Genetic Algorithm: NSGA-II"", IEEE Transactions On Evolutionary Computation, Vol. 6, No. 2, April 2002.")]
+  [StorableClass]
+  public class CrowdedComparisonSorter : SingleSuccessorOperator {
+
+    public IScopeTreeLookupParameter<IntValue> RankParameter {
+      get { return (IScopeTreeLookupParameter<IntValue>)Parameters["Rank"]; }
+    }
+
+    public IScopeTreeLookupParameter<DoubleValue> CrowdingDistanceParameter {
+      get { return (IScopeTreeLookupParameter<DoubleValue>)Parameters["CrowdingDistance"]; }
+    }
+
+    [StorableConstructor]
+    protected CrowdedComparisonSorter(bool deserializing) : base(deserializing) { }
+    protected CrowdedComparisonSorter(CrowdedComparisonSorter original, Cloner cloner) : base(original, cloner) { }
+    public CrowdedComparisonSorter() {
+      Parameters.Add(new ScopeTreeLookupParameter<IntValue>("Rank", "The rank of the solution."));
+      Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("CrowdingDistance", "The crowding distance of the solution."));
+    }
+
+    public override IOperation Apply() {
+      ItemArray<IntValue> ranks = RankParameter.ActualValue;
+      ItemArray<DoubleValue> distances = CrowdingDistanceParameter.ActualValue;
+      int size = ranks.Length;
+      int[] indices = Enumerable.Range(0, size).ToArray();
+
+      IScope[] scopes = ExecutionContext.Scope.SubScopes.ToArray();
+      Array.Sort(indices, scopes, new CustomComparer(ranks, distances));
+      ExecutionContext.Scope.SubScopes.Clear();
+      ExecutionContext.Scope.SubScopes.AddRange(scopes);
+      return base.Apply();
+    }
+
+    private class CustomComparer : IComparer<int> {
+      ItemArray<IntValue> ranks;
+      ItemArray<DoubleValue> distances;
+
+      public CustomComparer(ItemArray<IntValue> ranks, ItemArray<DoubleValue> distances) {
+        this.ranks = ranks;
+        this.distances = distances;
+      }
+
+      #region IComparer<int> Members
+
+      public int Compare(int x, int y) {
+        if (ranks[x].Value < ranks[y].Value) return -1;
+        else if (ranks[x].Value > ranks[y].Value) return 1;
+        else { // ranks are the same -> compare by distance
+          if (distances[x].Value > distances[y].Value) return -1;
+          else if (distances[x].Value < distances[y].Value) return 1;
+          else return 0; // same distance
+        }
+      }
+
+      #endregion
+    }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new CrowdedComparisonSorter(this, cloner);
+    }
+  }
+}
Index: /trunk/sources/HeuristicLab.Optimization.Operators/3.3/MultiObjective/CrowdingDistanceAssignment.cs
===================================================================
--- /trunk/sources/HeuristicLab.Optimization.Operators/3.3/MultiObjective/CrowdingDistanceAssignment.cs	(revision 5143)
+++ /trunk/sources/HeuristicLab.Optimization.Operators/3.3/MultiObjective/CrowdingDistanceAssignment.cs	(revision 5143)
@@ -0,0 +1,123 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using HeuristicLab.Common;
+using HeuristicLab.Core;
+using HeuristicLab.Data;
+using HeuristicLab.Operators;
+using HeuristicLab.Parameters;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+
+namespace HeuristicLab.Optimization.Operators {
+  [Item("CrowdingDistanceAssignment", "Calculates the crowding distances for each sub-scope as described in Deb et al. 2002. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), pp. 182-197.")]
+  [StorableClass]
+  public class CrowdingDistanceAssignment : SingleSuccessorOperator {
+
+    public ScopeTreeLookupParameter<DoubleArray> QualitiesParameter {
+      get { return (ScopeTreeLookupParameter<DoubleArray>)Parameters["Qualities"]; }
+    }
+
+    public ScopeTreeLookupParameter<DoubleValue> CrowdingDistanceParameter {
+      get { return (ScopeTreeLookupParameter<DoubleValue>)Parameters["CrowdingDistance"]; }
+    }
+
+    private void QualitiesParameter_DepthChanged(object sender, EventArgs e) {
+      CrowdingDistanceParameter.Depth = QualitiesParameter.Depth;
+    }
+
+    [StorableConstructor]
+    protected CrowdingDistanceAssignment(bool deserializing) : base(deserializing) { }
+    protected CrowdingDistanceAssignment(CrowdingDistanceAssignment original, Cloner cloner) : base(original, cloner) { }
+    public CrowdingDistanceAssignment() {
+      Parameters.Add(new ScopeTreeLookupParameter<DoubleArray>("Qualities", "The vector of quality values."));
+      Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("CrowdingDistance", "Sets the crowding distance in each sub-scope."));
+      AttachEventHandlers();
+    }
+
+    [StorableHook(HookType.AfterDeserialization)]
+    private void AttachEventHandlers() {
+      QualitiesParameter.DepthChanged += new EventHandler(QualitiesParameter_DepthChanged);
+    }
+
+    public static void Apply(DoubleArray[] qualities, DoubleValue[] distances) {
+      int populationSize = qualities.Length;
+      int objectiveCount = qualities[0].Length;
+      for (int m = 0; m < objectiveCount; m++) {
+        Array.Sort<DoubleArray, DoubleValue>(qualities, distances, new QualitiesComparer(m));
+
+        distances[0].Value = double.MaxValue;
+        distances[populationSize - 1].Value = double.MaxValue;
+
+        double minQuality = qualities[0][m];
+        double maxQuality = qualities[populationSize - 1][m];
+        for (int i = 1; i < populationSize - 1; i++) {
+          distances[i].Value += (qualities[i + 1][m] - qualities[i - 1][m]) / (maxQuality - minQuality);
+        }
+      }
+    }
+
+    public override IOperation Apply() {
+      DoubleArray[] qualities = QualitiesParameter.ActualValue.ToArray();
+      int populationSize = qualities.Length;
+      DoubleValue[] distances = new DoubleValue[populationSize];
+      for (int i = 0; i < populationSize; i++)
+        distances[i] = new DoubleValue(0);
+
+      CrowdingDistanceParameter.ActualValue = new ItemArray<DoubleValue>(distances);
+
+      Apply(qualities, distances);
+
+      return base.Apply();
+    }
+
+    private void Initialize(ItemArray<DoubleValue> distances) {
+      for (int i = 0; i < distances.Length; i++) {
+        if (distances[i] == null) distances[i] = new DoubleValue(0);
+        else distances[i].Value = 0;
+      }
+    }
+
+    private class QualitiesComparer : IComparer<DoubleArray> {
+      private int index;
+
+      public QualitiesComparer(int index) {
+        this.index = index;
+      }
+
+      #region IComparer<DoubleArray> Members
+
+      public int Compare(DoubleArray x, DoubleArray y) {
+        if (x[index] < y[index]) return -1;
+        else if (x[index] > y[index]) return +1;
+        else return 0;
+      }
+
+      #endregion
+    }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new CrowdingDistanceAssignment(this, cloner);
+    }
+  }
+}
Index: /trunk/sources/HeuristicLab.Optimization.Operators/3.3/MultiObjective/FastNonDominatedSort.cs
===================================================================
--- /trunk/sources/HeuristicLab.Optimization.Operators/3.3/MultiObjective/FastNonDominatedSort.cs	(revision 5143)
+++ /trunk/sources/HeuristicLab.Optimization.Operators/3.3/MultiObjective/FastNonDominatedSort.cs	(revision 5143)
@@ -0,0 +1,162 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+using HeuristicLab.Common;
+using HeuristicLab.Core;
+using HeuristicLab.Data;
+using HeuristicLab.Operators;
+using HeuristicLab.Parameters;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+
+namespace HeuristicLab.Optimization.Operators {
+  /// <summary>
+  /// FastNonDominatedSort as described in: Deb, Pratap, Agrawal and Meyarivan, "A Fast and Elitist Multiobjective
+  /// Genetic Algorithm: NSGA-II", IEEE Transactions On Evolutionary Computation, Vol. 6, No. 2, April 2002
+  /// </summary>
+  [Item("FastNonDominatedSort", @"FastNonDominatedSort as described in: Deb, Pratap, Agrawal and Meyarivan, ""A Fast and Elitist Multiobjective
+Genetic Algorithm: NSGA-II"", IEEE Transactions On Evolutionary Computation, Vol. 6, No. 2, April 2002")]
+  [StorableClass]
+  public class FastNonDominatedSort : SingleSuccessorOperator {
+    private enum DominationResult { Dominates, IsDominated, IsNonDominated };
+
+    public IValueLookupParameter<BoolArray> MaximizationParameter {
+      get { return (IValueLookupParameter<BoolArray>)Parameters["Maximization"]; }
+    }
+
+    public IScopeTreeLookupParameter<DoubleArray> QualitiesParameter {
+      get { return (IScopeTreeLookupParameter<DoubleArray>)Parameters["Qualities"]; }
+    }
+
+    public IScopeTreeLookupParameter<IntValue> RankParameter {
+      get { return (IScopeTreeLookupParameter<IntValue>)Parameters["Rank"]; }
+    }
+
+    [StorableConstructor]
+    protected FastNonDominatedSort(bool deserializing) : base(deserializing) { }
+    protected FastNonDominatedSort(FastNonDominatedSort original, Cloner cloner) : base(original, cloner) { }
+    public FastNonDominatedSort() {
+      Parameters.Add(new ValueLookupParameter<BoolArray>("Maximization", "Whether each objective is maximization or minimization."));
+      Parameters.Add(new ScopeTreeLookupParameter<DoubleArray>("Qualities", "The qualities of a solution.", 1));
+      Parameters.Add(new ScopeTreeLookupParameter<IntValue>("Rank", "The rank of a solution.", 1));
+    }
+
+    public override IOperation Apply() {
+      BoolArray maximization = MaximizationParameter.ActualValue;
+      ItemArray<DoubleArray> qualities = QualitiesParameter.ActualValue;
+      if (qualities == null) throw new InvalidOperationException(Name + ": No qualities found.");
+
+      IScope scope = ExecutionContext.Scope;
+      int populationSize = scope.SubScopes.Count;
+
+      List<ScopeList> fronts = new List<ScopeList>();
+      Dictionary<IScope, List<int>> dominatedScopes = new Dictionary<IScope, List<int>>();
+      int[] dominationCounter = new int[populationSize];
+      ItemArray<IntValue> rank = new ItemArray<IntValue>(populationSize);
+
+      for (int pI = 0; pI < populationSize - 1; pI++) {
+        IScope p = scope.SubScopes[pI];
+        if (!dominatedScopes.ContainsKey(p))
+          dominatedScopes[p] = new List<int>();
+        for (int qI = pI + 1; qI < populationSize; qI++) {
+          DominationResult test = Dominates(qualities[pI], qualities[qI], maximization);
+          if (test == DominationResult.Dominates) {
+            dominatedScopes[p].Add(qI);
+            dominationCounter[qI] += 1;
+          } else if (test == DominationResult.IsDominated) {
+            dominationCounter[pI] += 1;
+            if (!dominatedScopes.ContainsKey(scope.SubScopes[qI]))
+              dominatedScopes.Add(scope.SubScopes[qI], new List<int>());
+            dominatedScopes[scope.SubScopes[qI]].Add(pI);
+          }
+          if (pI == populationSize - 2
+            && qI == populationSize - 1
+            && dominationCounter[qI] == 0) {
+            rank[qI] = new IntValue(0);
+            AddToFront(scope.SubScopes[qI], fronts, 0);
+          }
+        }
+        if (dominationCounter[pI] == 0) {
+          rank[pI] = new IntValue(0);
+          AddToFront(p, fronts, 0);
+        }
+      }
+      int i = 0;
+      while (i < fronts.Count && fronts[i].Count > 0) {
+        ScopeList nextFront = new ScopeList();
+        foreach (IScope p in fronts[i]) {
+          if (dominatedScopes.ContainsKey(p)) {
+            for (int k = 0; k < dominatedScopes[p].Count; k++) {
+              int dominatedScope = dominatedScopes[p][k];
+              dominationCounter[dominatedScope] -= 1;
+              if (dominationCounter[dominatedScope] == 0) {
+                rank[dominatedScope] = new IntValue(i + 1);
+                nextFront.Add(scope.SubScopes[dominatedScope]);
+              }
+            }
+          }
+        }
+        i += 1;
+        fronts.Add(nextFront);
+      }
+
+      RankParameter.ActualValue = rank;
+
+      scope.SubScopes.Clear();
+
+      for (i = 0; i < fronts.Count; i++) {
+        Scope frontScope = new Scope("Front " + i);
+        foreach (var p in fronts[i])
+          frontScope.SubScopes.Add(p);
+        if (frontScope.SubScopes.Count > 0)
+          scope.SubScopes.Add(frontScope);
+      }
+      return base.Apply();
+    }
+
+    private DominationResult Dominates(DoubleArray left, DoubleArray right, BoolArray maximizations) {
+      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 bool IsDominated(double left, double right, bool maximization) {
+      return maximization && left < right
+        || !maximization && left > right;
+    }
+
+    private void AddToFront(IScope p, List<ScopeList> fronts, int i) {
+      if (i == fronts.Count) fronts.Add(new ScopeList());
+      fronts[i].Add(p);
+    }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new FastNonDominatedSort(this, cloner);
+    }
+  }
+}
Index: /trunk/sources/HeuristicLab.Optimization.Operators/3.3/MultiObjective/RankAndCrowdingSorter.cs
===================================================================
--- /trunk/sources/HeuristicLab.Optimization.Operators/3.3/MultiObjective/RankAndCrowdingSorter.cs	(revision 5143)
+++ /trunk/sources/HeuristicLab.Optimization.Operators/3.3/MultiObjective/RankAndCrowdingSorter.cs	(revision 5143)
@@ -0,0 +1,84 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using HeuristicLab.Common;
+using HeuristicLab.Core;
+using HeuristicLab.Data;
+using HeuristicLab.Operators;
+using HeuristicLab.Parameters;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Selection;
+
+namespace HeuristicLab.Optimization.Operators {
+  public class RankAndCrowdingSorter : AlgorithmOperator {
+    public ValueLookupParameter<BoolArray> MaximizationParameter {
+      get { return (ValueLookupParameter<BoolArray>)Parameters["Maximization"]; }
+    }
+    public ScopeTreeLookupParameter<DoubleArray> QualitiesParameter {
+      get { return (ScopeTreeLookupParameter<DoubleArray>)Parameters["Qualities"]; }
+    }
+    public ScopeTreeLookupParameter<IntValue> RankParameter {
+      get { return (ScopeTreeLookupParameter<IntValue>)Parameters["Rank"]; }
+    }
+    public ScopeTreeLookupParameter<DoubleValue> CrowdingDistanceParameter {
+      get { return (ScopeTreeLookupParameter<DoubleValue>)Parameters["CrowdingDistance"]; }
+    }
+
+    [StorableConstructor]
+    protected RankAndCrowdingSorter(bool deserializing) : base(deserializing) { }
+    protected RankAndCrowdingSorter(RankAndCrowdingSorter original, Cloner cloner) : base(original, cloner) { }
+    public RankAndCrowdingSorter()
+      : base() {
+      Parameters.Add(new ValueLookupParameter<BoolArray>("Maximization", "For each objective a value that is true if that objective should be maximized, or false if it should be minimized."));
+      Parameters.Add(new ScopeTreeLookupParameter<DoubleArray>("Qualities", "The vector of quality values."));
+      Parameters.Add(new ScopeTreeLookupParameter<IntValue>("Rank", "The rank of a solution (to which front it belongs)."));
+      Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("CrowdingDistance", "The crowding distance of a solution in a population."));
+
+      FastNonDominatedSort fastNonDominatedSort = new FastNonDominatedSort();
+      UniformSubScopesProcessor subScopesProcessor = new UniformSubScopesProcessor();
+      CrowdingDistanceAssignment crowdingDistanceAssignment = new CrowdingDistanceAssignment();
+      CrowdedComparisonSorter crowdedComparisonSorter = new CrowdedComparisonSorter();
+      MergingReducer mergingReducer = new MergingReducer();
+
+      fastNonDominatedSort.MaximizationParameter.ActualName = MaximizationParameter.Name;
+      fastNonDominatedSort.QualitiesParameter.ActualName = QualitiesParameter.Name;
+      fastNonDominatedSort.RankParameter.ActualName = RankParameter.Name;
+
+      crowdingDistanceAssignment.CrowdingDistanceParameter.ActualName = CrowdingDistanceParameter.Name;
+      crowdingDistanceAssignment.QualitiesParameter.ActualName = QualitiesParameter.Name;
+
+      crowdedComparisonSorter.CrowdingDistanceParameter.ActualName = CrowdingDistanceParameter.Name;
+      crowdedComparisonSorter.RankParameter.ActualName = RankParameter.Name;
+
+      OperatorGraph.InitialOperator = fastNonDominatedSort;
+      fastNonDominatedSort.Successor = subScopesProcessor;
+      subScopesProcessor.Operator = crowdingDistanceAssignment;
+      crowdingDistanceAssignment.Successor = crowdedComparisonSorter;
+      crowdedComparisonSorter.Successor = null;
+      subScopesProcessor.Successor = mergingReducer;
+      mergingReducer.Successor = null;
+    }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new RankAndCrowdingSorter(this, cloner);
+    }
+  }
+}
Index: /trunk/sources/HeuristicLab.Optimization.Operators/3.3/ParentCopyCrossover.cs
===================================================================
--- /trunk/sources/HeuristicLab.Optimization.Operators/3.3/ParentCopyCrossover.cs	(revision 5143)
+++ /trunk/sources/HeuristicLab.Optimization.Operators/3.3/ParentCopyCrossover.cs	(revision 5143)
@@ -0,0 +1,60 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using HeuristicLab.Common;
+using HeuristicLab.Core;
+using HeuristicLab.Operators;
+using HeuristicLab.Parameters;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+
+namespace HeuristicLab.Optimization.Operators {
+  [Item("ParentCopyCrossover", "This operator creates an offspring by creating a clone of a randomly chosen parent. It can be used in situations where no crossover should occur after selection.")]
+  [StorableClass]
+  public class ParentCopyCrossover : SingleSuccessorOperator, ICrossover, IStochasticOperator {
+    public ILookupParameter<IRandom> RandomParameter {
+      get { return (ILookupParameter<IRandom>)Parameters["Random"]; }
+    }
+
+
+    [StorableConstructor]
+    protected ParentCopyCrossover(bool deserializing) : base(deserializing) { }
+    protected ParentCopyCrossover(ParentCopyCrossover original, Cloner cloner) : base(original, cloner) { }
+    public ParentCopyCrossover()
+      : base() {
+      Parameters.Add(new LookupParameter<IRandom>("Random", "The random number generator."));
+    }
+
+    public override IOperation Apply() {
+      IScope scope = ExecutionContext.Scope;
+      int index = RandomParameter.ActualValue.Next(scope.SubScopes.Count);
+      IScope child = scope.SubScopes[index];
+
+      foreach (IVariable var in child.Variables)
+        scope.Variables.Add((IVariable)var.Clone());
+
+      return base.Apply();
+    }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new ParentCopyCrossover(this, cloner);
+    }
+  }
+}
Index: /trunk/sources/HeuristicLab.Optimizer/3.3/HeuristicLab.Optimizer-3.3.csproj
===================================================================
--- /trunk/sources/HeuristicLab.Optimizer/3.3/HeuristicLab.Optimizer-3.3.csproj	(revision 5142)
+++ /trunk/sources/HeuristicLab.Optimizer/3.3/HeuristicLab.Optimizer-3.3.csproj	(revision 5143)
@@ -110,6 +110,12 @@
   </ItemGroup>
   <ItemGroup>
+    <EmbeddedResource Include="DisposeViewTest.resx">
+      <DependentUpon>DisposeViewTest.cs</DependentUpon>
+    </EmbeddedResource>
     <EmbeddedResource Include="Documents\GA_VRP.hl" />
     <EmbeddedResource Include="Documents\SGP_SymbClass_Mammographic.hl" />
+    <EmbeddedResource Include="ImageTestView.resx">
+      <DependentUpon>ImageTestView.cs</DependentUpon>
+    </EmbeddedResource>
     <None Include="HeuristicLabOptimizerPlugin.cs.frame" />
     <Compile Include="CreateExperimentDialog.cs">
@@ -119,4 +125,10 @@
       <DependentUpon>CreateExperimentDialog.cs</DependentUpon>
     </Compile>
+    <Compile Include="DisposeViewTest.cs">
+      <SubType>UserControl</SubType>
+    </Compile>
+    <Compile Include="DisposeViewTest.Designer.cs">
+      <DependentUpon>DisposeViewTest.cs</DependentUpon>
+    </Compile>
     <Compile Include="FileManager.cs" />
     <Compile Include="ChartControlsWarning.cs">
@@ -126,7 +138,15 @@
       <DependentUpon>ChartControlsWarning.cs</DependentUpon>
     </Compile>
+    <Compile Include="ImageTest.cs" />
+    <Compile Include="ImageTestView.cs">
+      <SubType>UserControl</SubType>
+    </Compile>
+    <Compile Include="ImageTestView.Designer.cs">
+      <DependentUpon>ImageTestView.cs</DependentUpon>
+    </Compile>
     <Compile Include="MenuItems\ClipboardMenuItem.cs" />
     <Compile Include="MenuItems\AboutMenuItem.cs" />
     <Compile Include="MenuItems\ConvertIntoUserDefinedAlgorithmMenuItem.cs" />
+    <Compile Include="MenuItems\ImageTestMenuItem.cs" />
     <Compile Include="MenuItems\UsernamePasswordMenuItem.cs" />
     <Compile Include="MenuItems\CreateExperimentMenuItem.cs" />
@@ -138,4 +158,5 @@
       <DependentUpon>Settings.settings</DependentUpon>
     </Compile>
+    <Compile Include="SimpleItem.cs" />
     <Compile Include="StartPage.cs">
       <SubType>UserControl</SubType>
Index: /trunk/sources/HeuristicLab.Selection/3.3/CrowdedTournamentSelector.cs
===================================================================
--- /trunk/sources/HeuristicLab.Selection/3.3/CrowdedTournamentSelector.cs	(revision 5143)
+++ /trunk/sources/HeuristicLab.Selection/3.3/CrowdedTournamentSelector.cs	(revision 5143)
@@ -0,0 +1,118 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System.Collections.Generic;
+using System.Linq;
+using HeuristicLab.Common;
+using HeuristicLab.Core;
+using HeuristicLab.Data;
+using HeuristicLab.Optimization;
+using HeuristicLab.Parameters;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+
+namespace HeuristicLab.Selection {
+  [Item("CrowdedTournamentSelector", "Selects solutions using tournament selection by using the partial order defined in Deb et al. 2002. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), pp. 182-197.")]
+  [StorableClass]
+  public class CrowdedTournamentSelector : Selector, IMultiObjectiveSelector, IStochasticOperator {
+    public ILookupParameter<BoolArray> MaximizationParameter {
+      get { return (ILookupParameter<BoolArray>)Parameters["Maximization"]; }
+    }
+    public IValueLookupParameter<IntValue> NumberOfSelectedSubScopesParameter {
+      get { return (IValueLookupParameter<IntValue>)Parameters["NumberOfSelectedSubScopes"]; }
+    }
+    public IValueParameter<BoolValue> CopySelectedParameter {
+      get { return (IValueParameter<BoolValue>)Parameters["CopySelected"]; }
+    }
+    public ILookupParameter<IRandom> RandomParameter {
+      get { return (ILookupParameter<IRandom>)Parameters["Random"]; }
+    }
+    public ILookupParameter<ItemArray<DoubleArray>> QualitiesParameter {
+      get { return (ILookupParameter<ItemArray<DoubleArray>>)Parameters["Qualities"]; }
+    }
+    public IScopeTreeLookupParameter<IntValue> RankParameter {
+      get { return (IScopeTreeLookupParameter<IntValue>)Parameters["Rank"]; }
+    }
+    public IScopeTreeLookupParameter<DoubleValue> CrowdingDistanceParameter {
+      get { return (IScopeTreeLookupParameter<DoubleValue>)Parameters["CrowdingDistance"]; }
+    }
+    public IValueLookupParameter<IntValue> GroupSizeParameter {
+      get { return (IValueLookupParameter<IntValue>)Parameters["GroupSize"]; }
+    }
+
+    public BoolValue CopySelected {
+      get { return CopySelectedParameter.Value; }
+      set { CopySelectedParameter.Value = value; }
+    }
+
+    [StorableConstructor]
+    protected CrowdedTournamentSelector(bool deserializing) : base(deserializing) { }
+    protected CrowdedTournamentSelector(CrowdedTournamentSelector original, Cloner cloner) : base(original, cloner) { }
+    public CrowdedTournamentSelector()
+      : base() {
+      Parameters.Add(new LookupParameter<BoolArray>("Maximization", "For each objective determines whether it should be maximized or minimized."));
+      Parameters.Add(new ValueLookupParameter<IntValue>("NumberOfSelectedSubScopes", "The number of sub-scopes that should be selected."));
+      Parameters.Add(new ValueParameter<BoolValue>("CopySelected", "True if the selected scopes are to be copied (cloned) otherwise they're moved."));
+      Parameters.Add(new LookupParameter<IRandom>("Random", "The random number generator."));
+      Parameters.Add(new ScopeTreeLookupParameter<DoubleArray>("Qualities", "The solutions' qualities vector."));
+      Parameters.Add(new ScopeTreeLookupParameter<IntValue>("Rank", "The solutions' domination rank."));
+      Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("CrowdingDistance", "The solutions' crowding distance values."));
+      Parameters.Add(new ValueLookupParameter<IntValue>("GroupSize", "The size of the group from which the best will be chosen.", new IntValue(2)));
+    }
+
+    protected override IScope[] Select(List<IScope> scopes) {
+      IRandom random = RandomParameter.ActualValue;
+      List<int> ranks = RankParameter.ActualValue.Select(x => x.Value).ToList();
+      List<double> crowdingDistance = CrowdingDistanceParameter.ActualValue.Select(x => x.Value).ToList();
+      int count = NumberOfSelectedSubScopesParameter.ActualValue.Value;
+      int groupSize = GroupSizeParameter.ActualValue.Value;
+      bool copy = CopySelected.Value;
+      IScope[] selected = new IScope[count];
+
+      for (int i = 0; i < count; i++) {
+        int best = random.Next(scopes.Count);
+        int index;
+        for (int j = 1; j < groupSize; j++) {
+          index = random.Next(scopes.Count);
+          if (ranks[best] > ranks[index]
+            || ranks[best] == ranks[index]
+              && crowdingDistance[best] < crowdingDistance[index]) {
+            best = index;
+          }
+        }
+
+        if (copy)
+          selected[i] = (IScope)scopes[best].Clone();
+        else {
+          selected[i] = scopes[best];
+          scopes.RemoveAt(best);
+          ranks.RemoveAt(best);
+          crowdingDistance.RemoveAt(best);
+        }
+      }
+
+      return selected;
+    }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new CrowdedTournamentSelector(this, cloner);
+    }
+  }
+}
Index: /trunk/sources/HeuristicLab.Selection/3.3/HeuristicLab.Selection-3.3.csproj
===================================================================
--- /trunk/sources/HeuristicLab.Selection/3.3/HeuristicLab.Selection-3.3.csproj	(revision 5142)
+++ /trunk/sources/HeuristicLab.Selection/3.3/HeuristicLab.Selection-3.3.csproj	(revision 5143)
@@ -106,4 +106,5 @@
     <Compile Include="BestSelector.cs" />
     <Compile Include="ConditionalSelector.cs" />
+    <Compile Include="CrowdedTournamentSelector.cs" />
     <Compile Include="LeftSelector.cs" />
     <Compile Include="RandomReplacer.cs" />
Index: /trunk/sources/HeuristicLab/3.3/Files.txt
===================================================================
--- /trunk/sources/HeuristicLab/3.3/Files.txt	(revision 5142)
+++ /trunk/sources/HeuristicLab/3.3/Files.txt	(revision 5143)
@@ -9,4 +9,5 @@
 HeuristicLab.Algorithms.GeneticAlgorithm\3.3:HeuristicLab.Algorithms.GeneticAlgorithm-3.3.dll
 HeuristicLab.Algorithms.LocalSearch\3.3:HeuristicLab.Algorithms.LocalSearch-3.3.dll
+HeuristicLab.Algorithms.NSGA2\3.3:HeuristicLab.Algorithms.NSGA2-3.3.dll
 HeuristicLab.Algorithms.OffspringSelectionGeneticAlgorithm\3.3:HeuristicLab.Algorithms.OffspringSelectionGeneticAlgorithm-3.3.dll
 HeuristicLab.Algorithms.SimulatedAnnealing\3.3:HeuristicLab.Algorithms.SimulatedAnnealing-3.3.dll
