Index: stable/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression-3.4.csproj
===================================================================
--- stable/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression-3.4.csproj	(revision 10508)
+++ stable/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression-3.4.csproj	(revision 10509)
@@ -121,4 +121,5 @@
     <Compile Include="SingleObjective\ConstantOptimizationAnalyzer.cs" />
     <Compile Include="SingleObjective\Evaluators\SymbolicRegressionMeanRelativeErrorEvaluator.cs" />
+    <Compile Include="SingleObjective\Evaluators\SymbolicRegressionLogResidualEvaluator.cs" />
     <Compile Include="SingleObjective\SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer.cs" />
     <Compile Include="SingleObjective\SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer.cs" />
Index: stable/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionLogResidualEvaluator.cs
===================================================================
--- stable/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionLogResidualEvaluator.cs	(revision 10509)
+++ stable/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionLogResidualEvaluator.cs	(revision 10509)
@@ -0,0 +1,93 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2013 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.Encodings.SymbolicExpressionTreeEncoding;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+
+namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
+  [Item("Log Residual Evaluator", "Evaluator for symbolic regression models that calculates the mean of logarithmic absolute residuals avg(log( 1 + abs(y' - y)))" + 
+                                  "This evaluator does not perform linear scaling!" +
+                                  "This evaluator can be useful if the modeled function contains discontinuities (e.g. 1/x). " +
+                                  "For some data sets (e.g. Korns benchmark instances containing inverses of near zero values) the squared error or absolute " +
+                                  "error put too much emphasis on modeling the outlier values. Using log-residuals instead has the " +
+                                  "effect that smaller residuals have a stronger impact on the total quality compared to the large residuals." +
+                                  "This effects GP convergence because functional fragments which are necessary to explain small variations are also more likely" +
+                                  " to stay in the population. This is useful even when the actual objective function is mean of squared errors.")]
+  [StorableClass]
+  public class SymbolicRegressionLogResidualEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
+    [StorableConstructor]
+    protected SymbolicRegressionLogResidualEvaluator(bool deserializing) : base(deserializing) { }
+    protected SymbolicRegressionLogResidualEvaluator(SymbolicRegressionLogResidualEvaluator original, Cloner cloner)
+      : base(original, cloner) {
+    }
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new SymbolicRegressionLogResidualEvaluator(this, cloner);
+    }
+
+    public SymbolicRegressionLogResidualEvaluator() : base() { }
+
+    public override bool Maximization { get { return false; } }
+
+    public override IOperation InstrumentedApply() {
+      var solution = SymbolicExpressionTreeParameter.ActualValue;
+      IEnumerable<int> rows = GenerateRowsToEvaluate();
+
+      double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows);
+      QualityParameter.ActualValue = new DoubleValue(quality);
+
+      return base.InstrumentedApply();
+    }
+
+    public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows) {
+      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
+      IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
+      IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
+
+      var logRes = boundedEstimatedValues.Zip(targetValues, (e, t) => Math.Log(1.0 + Math.Abs(e - t)));
+
+      OnlineCalculatorError errorState;
+      OnlineCalculatorError varErrorState;
+      double mlr;
+      double variance;
+      OnlineMeanAndVarianceCalculator.Calculate(logRes, out mlr, out variance, out errorState, out varErrorState);
+      if (errorState != OnlineCalculatorError.None) return double.NaN;
+      return mlr;
+    }
+
+    public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
+      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
+      EstimationLimitsParameter.ExecutionContext = context;
+
+      double mlr = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows);
+
+      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
+      EstimationLimitsParameter.ExecutionContext = null;
+
+      return mlr;
+    }
+  }
+}
