Index: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/MultiObjective/SymbolicClassificationMultiObjectiveMeanSquaredErrorTreeSizeEvaluator.cs
===================================================================
--- trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/MultiObjective/SymbolicClassificationMultiObjectiveMeanSquaredErrorTreeSizeEvaluator.cs	(revision 5882)
+++ trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/MultiObjective/SymbolicClassificationMultiObjectiveMeanSquaredErrorTreeSizeEvaluator.cs	(revision 5894)
@@ -58,5 +58,7 @@
       IEnumerable<double> originalValues = problemData.Dataset.GetEnumeratedVariableValues(problemData.TargetVariable, rows);
       IEnumerable<double> boundedEstimationValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
-      double mse = OnlineMeanSquaredErrorEvaluator.Calculate(originalValues, boundedEstimationValues);
+      OnlineEvaluatorError errorState;
+      double mse = OnlineMeanSquaredErrorEvaluator.Calculate(originalValues, boundedEstimationValues, out errorState);
+      if (errorState != OnlineEvaluatorError.None) mse = double.NaN;
       return new double[2] { mse, solution.Length };
     }
@@ -72,5 +74,5 @@
       SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
       EstimationLimitsParameter.ExecutionContext = null;
-      EvaluatedNodesParameter.ExecutionContext = null; 
+      EvaluatedNodesParameter.ExecutionContext = null;
 
       return quality;
Index: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/MultiObjective/SymbolicClassificationMultiObjectivePearsonRSquaredTreeSizeEvaluator.cs
===================================================================
--- trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/MultiObjective/SymbolicClassificationMultiObjectivePearsonRSquaredTreeSizeEvaluator.cs	(revision 5882)
+++ trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/MultiObjective/SymbolicClassificationMultiObjectivePearsonRSquaredTreeSizeEvaluator.cs	(revision 5894)
@@ -37,6 +37,9 @@
       IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
       IEnumerable<double> originalValues = problemData.Dataset.GetEnumeratedVariableValues(problemData.TargetVariable, rows);
-      double r2 = OnlinePearsonsRSquaredEvaluator.Calculate(estimatedValues, originalValues);
-      return new double[] { double.IsNaN(r2) ? 0.0 : r2, solution.Length };
+      OnlineEvaluatorError errorState;
+      double r2 = OnlinePearsonsRSquaredEvaluator.Calculate(estimatedValues, originalValues, out errorState);
+      if (errorState != OnlineEvaluatorError.None) r2 = 0.0;
+      return new double[] { r2, solution.Length };
+
     }
 
Index: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveBoundedMeanSquaredErrorEvaluator.cs
===================================================================
--- trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveBoundedMeanSquaredErrorEvaluator.cs	(revision 5882)
+++ trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveBoundedMeanSquaredErrorEvaluator.cs	(revision 5894)
@@ -63,5 +63,4 @@
       IEnumerator<double> originalEnumerator = originalValues.GetEnumerator();
       IEnumerator<double> estimatedEnumerator = estimatedValues.GetEnumerator();
-      OnlineMeanSquaredErrorEvaluator mseEvaluator = new OnlineMeanSquaredErrorEvaluator();
       double errorSum = 0.0;
       int n = 0;
Index: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator.cs
===================================================================
--- trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator.cs	(revision 5882)
+++ trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator.cs	(revision 5894)
@@ -58,5 +58,8 @@
       IEnumerable<double> originalValues = problemData.Dataset.GetEnumeratedVariableValues(problemData.TargetVariable, rows);
       IEnumerable<double> boundedEstimationValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
-      return OnlineMeanSquaredErrorEvaluator.Calculate(originalValues, boundedEstimationValues);
+      OnlineEvaluatorError errorState;
+      double mse = OnlineMeanSquaredErrorEvaluator.Calculate(originalValues, boundedEstimationValues, out errorState);
+      if (errorState != OnlineEvaluatorError.None) return double.NaN;
+      else return mse;
     }
 
Index: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectivePearsonRSquaredEvaluator.cs
===================================================================
--- trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectivePearsonRSquaredEvaluator.cs	(revision 5882)
+++ trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectivePearsonRSquaredEvaluator.cs	(revision 5894)
@@ -58,6 +58,8 @@
       IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
       IEnumerable<double> originalValues = problemData.Dataset.GetEnumeratedVariableValues(problemData.TargetVariable, rows);
-      double r2 = OnlinePearsonsRSquaredEvaluator.Calculate(estimatedValues, originalValues);
-      return double.IsNaN(r2) ? 0.0 : r2;
+      OnlineEvaluatorError errorState;
+      double r2 = OnlinePearsonsRSquaredEvaluator.Calculate(estimatedValues, originalValues, out errorState);
+      if (errorState != OnlineEvaluatorError.None) return 0.0;
+      else return r2;
     }
 
@@ -72,5 +74,5 @@
       SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
       EstimationLimitsParameter.ExecutionContext = null;
-      EvaluatedNodesParameter.ExecutionContext = null; 
+      EvaluatedNodesParameter.ExecutionContext = null;
 
       return r2;
Index: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SymbolicDiscriminantFunctionClassificationModel.cs
===================================================================
--- trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SymbolicDiscriminantFunctionClassificationModel.cs	(revision 5882)
+++ trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SymbolicDiscriminantFunctionClassificationModel.cs	(revision 5894)
@@ -124,5 +124,7 @@
       double alpha;
       double beta;
-      OnlineLinearScalingParameterCalculator.Calculate(estimatedValues, targetValues, out alpha, out beta);
+      OnlineEvaluatorError errorState;
+      OnlineLinearScalingParameterCalculator.Calculate(estimatedValues, targetValues, out alpha, out beta, out errorState);
+      if (errorState != OnlineEvaluatorError.None) return;
 
       ConstantTreeNode alphaTreeNode = null;
