Index: trunk/sources/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/Evaluators/MultiObjectiveSymbolicRegressionMeanSquaredErrorEvaluator.cs
===================================================================
--- trunk/sources/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/Evaluators/MultiObjectiveSymbolicRegressionMeanSquaredErrorEvaluator.cs	(revision 4166)
+++ trunk/sources/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/Evaluators/MultiObjectiveSymbolicRegressionMeanSquaredErrorEvaluator.cs	(revision 4166)
@@ -0,0 +1,74 @@
+#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.Encodings.SymbolicExpressionTreeEncoding;
+using HeuristicLab.Parameters;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Problems.DataAnalysis.Evaluators;
+using HeuristicLab.Problems.DataAnalysis.Symbolic;
+using HeuristicLab.Problems.DataAnalysis.Symbolic.Symbols;
+
+namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic {
+  [Item("MultiObjectiveSymbolicRegressionMeanSquaredErrorEvaluator", "Calculates the mean squared error and the number of variables of a symbolic regression solution.")]
+  [StorableClass]
+  public class MultiObjectiveSymbolicRegressionMeanSquaredErrorEvaluator : MultiObjectiveSymbolicRegressionEvaluator {
+    private const string UpperEstimationLimitParameterName = "UpperEstimationLimit";
+    private const string LowerEstimationLimitParameterName = "LowerEstimationLimit";
+
+    #region parameter properties
+    public IValueLookupParameter<DoubleValue> UpperEstimationLimitParameter {
+      get { return (IValueLookupParameter<DoubleValue>)Parameters[UpperEstimationLimitParameterName]; }
+    }
+    public IValueLookupParameter<DoubleValue> LowerEstimationLimitParameter {
+      get { return (IValueLookupParameter<DoubleValue>)Parameters[LowerEstimationLimitParameterName]; }
+    }
+    #endregion
+    #region properties
+    public DoubleValue UpperEstimationLimit {
+      get { return UpperEstimationLimitParameter.ActualValue; }
+    }
+    public DoubleValue LowerEstimationLimit {
+      get { return LowerEstimationLimitParameter.ActualValue; }
+    }
+    #endregion
+    public MultiObjectiveSymbolicRegressionMeanSquaredErrorEvaluator()
+      : base() {
+      Parameters.Add(new ValueLookupParameter<DoubleValue>(UpperEstimationLimitParameterName, "The upper limit that should be used as cut off value for the output values of symbolic expression trees."));
+      Parameters.Add(new ValueLookupParameter<DoubleValue>(LowerEstimationLimitParameterName, "The lower limit that should be used as cut off value for the output values of symbolic expression trees."));
+    }
+
+    protected override double[] Evaluate(ISymbolicExpressionTreeInterpreter interpreter, SymbolicExpressionTree solution, Dataset dataset, StringValue targetVariable, IEnumerable<int> rows) {
+      double mse = SymbolicRegressionMeanSquaredErrorEvaluator.Calculate(interpreter, solution, LowerEstimationLimit.Value, UpperEstimationLimit.Value, dataset, targetVariable.Value, rows);
+      List<string> vars = new List<string>();
+      solution.Root.ForEachNodePostfix(n => {
+        var varNode = n as VariableTreeNode;
+        if (varNode != null && !vars.Contains(varNode.VariableName)) {
+          vars.Add(varNode.VariableName);
+        }
+      });
+      return new double[2] { mse, vars.Count };
+    }
+  }
+}
