Index: branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/PearsonRSquaredNestedTreeSizeEvaluator.cs
===================================================================
--- branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/PearsonRSquaredNestedTreeSizeEvaluator.cs	(revision 12142)
+++ branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/PearsonRSquaredNestedTreeSizeEvaluator.cs	(revision 12147)
@@ -20,4 +20,5 @@
 #endregion
 
+using System;
 using System.Collections.Generic;
 using System.Linq;
@@ -77,4 +78,5 @@
     public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling) {
       double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling);
+      r2 = Math.Round(r2, 3);
       return new double[2] { r2, solution.IterateNodesPostfix().Sum(n => n.GetLength()) };
     }
Index: branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/PearsonRSquaredNumberOfVariablesEvaluator.cs
===================================================================
--- branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/PearsonRSquaredNumberOfVariablesEvaluator.cs	(revision 12147)
+++ branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/PearsonRSquaredNumberOfVariablesEvaluator.cs	(revision 12147)
@@ -0,0 +1,98 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2014 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.Security.Cryptography;
+using HeuristicLab.Common;
+using HeuristicLab.Core;
+using HeuristicLab.Data;
+using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
+using HeuristicLab.Parameters;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+
+namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
+  [Item("Pearson R² & Number of Variables Evaluator", "Calculates the Pearson R² and the number of used variables of a symbolic regression solution.")]
+  [StorableClass]
+  public class PearsonRSquaredNumberOfVariablesEvaluator : SymbolicRegressionMultiObjectiveEvaluator {
+    private const string useConstantOptimizationParameterName = "Use constant optimization";
+    public IFixedValueParameter<BoolValue> UseConstantOptimizationParameter {
+      get { return (IFixedValueParameter<BoolValue>)Parameters[useConstantOptimizationParameterName]; }
+    }
+    public bool UseConstantOptimization {
+      get { return UseConstantOptimizationParameter.Value.Value; }
+      set { UseConstantOptimizationParameter.Value.Value = value; }
+    }
+
+    [StorableConstructor]
+    protected PearsonRSquaredNumberOfVariablesEvaluator(bool deserializing) : base(deserializing) { }
+    protected PearsonRSquaredNumberOfVariablesEvaluator(PearsonRSquaredNumberOfVariablesEvaluator original, Cloner cloner)
+      : base(original, cloner) {
+    }
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new PearsonRSquaredNumberOfVariablesEvaluator(this, cloner);
+    }
+
+    public PearsonRSquaredNumberOfVariablesEvaluator()
+      : base() {
+      Parameters.Add(new FixedValueParameter<BoolValue>(useConstantOptimizationParameterName, "", new BoolValue(false)));
+    }
+
+    public override IEnumerable<bool> Maximization { get { return new bool[2] { true, false }; } }
+
+    public override IOperation InstrumentedApply() {
+      IEnumerable<int> rows = GenerateRowsToEvaluate();
+      var solution = SymbolicExpressionTreeParameter.ActualValue;
+      var problemData = ProblemDataParameter.ActualValue;
+      var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
+      var estimationLimits = EstimationLimitsParameter.ActualValue;
+      var applyLinearScaling = ApplyLinearScalingParameter.ActualValue.Value;
+
+      if (UseConstantOptimization) {
+        SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, solution, problemData, rows, applyLinearScaling, 5, estimationLimits.Upper, estimationLimits.Lower);
+      }
+      double[] qualities = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value);
+      QualitiesParameter.ActualValue = new DoubleArray(qualities);
+      return base.InstrumentedApply();
+    }
+
+    public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling) {
+      double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling);
+      r2 = Math.Round(r2, 3);
+      return new double[2] { r2, solution.IterateNodesPostfix().OfType<VariableTreeNode>().Count() };
+    }
+
+    public override double[] Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
+      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
+      EstimationLimitsParameter.ExecutionContext = context;
+      ApplyLinearScalingParameter.ExecutionContext = context;
+
+      double[] quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value);
+
+      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
+      EstimationLimitsParameter.ExecutionContext = null;
+      ApplyLinearScalingParameter.ExecutionContext = null;
+
+      return quality;
+    }
+  }
+}
Index: branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/PearsonRSquaredNumberofVariablesEvaluator.cs
===================================================================
--- branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/PearsonRSquaredNumberofVariablesEvaluator.cs	(revision 12142)
+++ 	(revision )
@@ -1,95 +1,0 @@
-﻿#region License Information
-/* HeuristicLab
- * Copyright (C) 2002-2014 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.Encodings.SymbolicExpressionTreeEncoding;
-using HeuristicLab.Parameters;
-using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
-
-namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
-  [Item("Pearson R² & Number of Variables Evaluator", "Calculates the Pearson R² and the number of used variables of a symbolic regression solution.")]
-  [StorableClass]
-  public class PearsonRSquaredNumberOfVariablesEvaluator : SymbolicRegressionMultiObjectiveEvaluator {
-    private const string useConstantOptimizationParameterName = "Use constant optimization";
-    public IFixedValueParameter<BoolValue> UseConstantOptimizationParameter {
-      get { return (IFixedValueParameter<BoolValue>)Parameters[useConstantOptimizationParameterName]; }
-    }
-    public bool UseConstantOptimization {
-      get { return UseConstantOptimizationParameter.Value.Value; }
-      set { UseConstantOptimizationParameter.Value.Value = value; }
-    }
-
-    [StorableConstructor]
-    protected PearsonRSquaredNumberOfVariablesEvaluator(bool deserializing) : base(deserializing) { }
-    protected PearsonRSquaredNumberOfVariablesEvaluator(PearsonRSquaredNumberOfVariablesEvaluator original, Cloner cloner)
-      : base(original, cloner) {
-    }
-    public override IDeepCloneable Clone(Cloner cloner) {
-      return new PearsonRSquaredNumberOfVariablesEvaluator(this, cloner);
-    }
-
-    public PearsonRSquaredNumberOfVariablesEvaluator()
-      : base() {
-      Parameters.Add(new FixedValueParameter<BoolValue>(useConstantOptimizationParameterName, "", new BoolValue(false)));
-    }
-
-    public override IEnumerable<bool> Maximization { get { return new bool[2] { true, false }; } }
-
-    public override IOperation InstrumentedApply() {
-      IEnumerable<int> rows = GenerateRowsToEvaluate();
-      var solution = SymbolicExpressionTreeParameter.ActualValue;
-      var problemData = ProblemDataParameter.ActualValue;
-      var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
-      var estimationLimits = EstimationLimitsParameter.ActualValue;
-      var applyLinearScaling = ApplyLinearScalingParameter.ActualValue.Value;
-
-      if (UseConstantOptimization) {
-        SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, solution, problemData, rows, applyLinearScaling, 5, estimationLimits.Upper, estimationLimits.Lower);
-      }
-      double[] qualities = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value);
-      QualitiesParameter.ActualValue = new DoubleArray(qualities);
-      return base.InstrumentedApply();
-    }
-
-    public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling) {
-      double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling);
-      return new double[2] { r2, solution.IterateNodesPostfix().OfType<VariableTreeNode>().Count() };
-    }
-
-    public override double[] Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
-      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
-      EstimationLimitsParameter.ExecutionContext = context;
-      ApplyLinearScalingParameter.ExecutionContext = context;
-
-      double[] quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value);
-
-      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
-      EstimationLimitsParameter.ExecutionContext = null;
-      ApplyLinearScalingParameter.ExecutionContext = null;
-
-      return quality;
-    }
-  }
-}
Index: branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/PearsonRSquaredTreeComplexityEvaluator.cs
===================================================================
--- branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/PearsonRSquaredTreeComplexityEvaluator.cs	(revision 12142)
+++ branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/PearsonRSquaredTreeComplexityEvaluator.cs	(revision 12147)
@@ -20,4 +20,5 @@
 #endregion
 
+using System;
 using System.Collections.Generic;
 using HeuristicLab.Common;
@@ -82,4 +83,5 @@
     public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling) {
       double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling);
+      r2 = Math.Round(r2, 3);
       return new double[2] { r2, SymbolicDataAnalysisModelComplexityAnalyzer.CalculateComplexity(solution.Root.GetSubtree(0).GetSubtree(0)) };
     }
Index: branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator.cs
===================================================================
--- branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator.cs	(revision 12142)
+++ branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator.cs	(revision 12147)
@@ -20,4 +20,5 @@
 #endregion
 
+using System;
 using System.Collections.Generic;
 using HeuristicLab.Common;
@@ -82,4 +83,5 @@
     public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling) {
       double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling);
+      r2 = Math.Round(r2, 3);
       return new double[2] { r2, solution.Length };
     }
