Changeset 9569 for trunk/sources/HeuristicLab.Algorithms.OffspringSelectionGeneticAlgorithm/3.3

Timestamp:

06/03/13 14:39:36 (11 years ago)

Author:

mkommend

Message:

#2038: Added reevaluation of elites in ES, IslandGA, IslandOSGA, OSGA, SASEGASA, and RAPGA.

Location:

trunk/sources/HeuristicLab.Algorithms.OffspringSelectionGeneticAlgorithm/3.3

Files:

• IslandOffspringSelectionGeneticAlgorithm.cs (modified) (5 diffs)
• IslandOffspringSelectionGeneticAlgorithmMainLoop.cs (modified) (4 diffs)
• OffspringSelectionGeneticAlgorithm.cs (modified) (5 diffs)
• OffspringSelectionGeneticAlgorithmMainLoop.cs (modified) (4 diffs)
• OffspringSelectionGeneticAlgorithmMainOperator.cs (modified) (6 diffs)
• SASEGASA.cs (modified) (5 diffs)
• SASEGASAMainLoop.cs (modified) (4 diffs)

trunk/sources/HeuristicLab.Algorithms.OffspringSelectionGeneticAlgorithm/3.3/IslandOffspringSelectionGeneticAlgorithm.cs

@@ -100 +100 @@
       get { return (ValueParameter<IntValue>)Parameters["Elites"]; }
     }
+    private IFixedValueParameter<BoolValue> ReevaluateElitesParameter {
+      get { return (IFixedValueParameter<BoolValue>)Parameters["ReevaluateElites"]; }
+    }
     private ValueLookupParameter<DoubleValue> SuccessRatioParameter {
       get { return (ValueLookupParameter<DoubleValue>)Parameters["SuccessRatio"]; }
@@ -192 +195 @@
       get { return ElitesParameter.Value; }
       set { ElitesParameter.Value = value; }
+    }
+    public bool ReevaluteElites {
+      get { return ReevaluateElitesParameter.Value.Value; }
+      set { ReevaluateElitesParameter.Value.Value = value; }
     }
     private DoubleValue SuccessRatio {
@@ -260 +267 @@
       if (successfulOffspringAnalyzer == null)
         successfulOffspringAnalyzer = new SuccessfulOffspringAnalyzer();
+      if (!Parameters.ContainsKey("ReevaluateElites")) {
+        Parameters.Add(new FixedValueParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)", (BoolValue)new BoolValue(false).AsReadOnly()) { Hidden = true });
+      }
       #endregion
 
@@ -293 +303 @@
       Parameters.Add(new OptionalConstrainedValueParameter<IManipulator>("Mutator", "The operator used to mutate solutions."));
       Parameters.Add(new ValueParameter<IntValue>("Elites", "The numer of elite solutions which are kept in each generation.", new IntValue(1)));
+      Parameters.Add(new FixedValueParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)", new BoolValue(false)) { Hidden = true });
       Parameters.Add(new ValueLookupParameter<DoubleValue>("SuccessRatio", "The ratio of successful to total children that should be achieved.", new DoubleValue(1)));
       Parameters.Add(new ValueLookupParameter<DoubleValue>("ComparisonFactorLowerBound", "The lower bound of the comparison factor (start).", new DoubleValue(0)));
@@ -355 +366 @@
       mainLoop.CrossoverParameter.ActualName = CrossoverParameter.Name;
       mainLoop.ElitesParameter.ActualName = ElitesParameter.Name;
+      mainLoop.ReevaluateElitesParameter.ActualName = ReevaluateElitesParameter.Name;
       mainLoop.MutatorParameter.ActualName = MutatorParameter.Name;
       mainLoop.MutationProbabilityParameter.ActualName = MutationProbabilityParameter.Name;

trunk/sources/HeuristicLab.Algorithms.OffspringSelectionGeneticAlgorithm/3.3/IslandOffspringSelectionGeneticAlgorithmMainLoop.cs

@@ -90 +90 @@
     public ValueLookupParameter<IntValue> ElitesParameter {
       get { return (ValueLookupParameter<IntValue>)Parameters["Elites"]; }
+    }
+    public IValueLookupParameter<BoolValue> ReevaluateElitesParameter {
+      get { return (IValueLookupParameter<BoolValue>)Parameters["ReevaluateElites"]; }
     }
     public ValueLookupParameter<ResultCollection> ResultsParameter {
@@ -158 +161 @@
       Parameters.Add(new ValueLookupParameter<IOperator>("Evaluator", "The operator used to evaluate solutions. This operator is executed in parallel, if an engine is used which supports parallelization."));
       Parameters.Add(new ValueLookupParameter<IntValue>("Elites", "The numer of elite solutions which are kept in each generation."));
+      Parameters.Add(new ValueLookupParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)"));
       Parameters.Add(new ValueLookupParameter<ResultCollection>("Results", "The results collection to store the results."));
       Parameters.Add(new ValueLookupParameter<IOperator>("Visualizer", "The operator used to visualize solutions."));
@@ -248 +252 @@
       mainOperator.CurrentSuccessRatioParameter.ActualName = "CurrentSuccessRatio";
       mainOperator.ElitesParameter.ActualName = ElitesParameter.Name;
+      mainOperator.ReevaluateElitesParameter.ActualName = ReevaluateElitesParameter.Name;
       mainOperator.EvaluatedSolutionsParameter.ActualName = EvaluatedSolutionsParameter.Name;
       mainOperator.EvaluatorParameter.ActualName = EvaluatorParameter.Name;
@@ -414 +419 @@
     }
 
+    [StorableHook(HookType.AfterDeserialization)]
+    private void AfterDeserialization() {
+      if (!Parameters.ContainsKey("ReevaluateElites")) {
+        Parameters.Add(new ValueLookupParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)"));
+      }
+    }
+
     public override IOperation Apply() {
       if (CrossoverParameter.ActualValue == null)

trunk/sources/HeuristicLab.Algorithms.OffspringSelectionGeneticAlgorithm/3.3/OffspringSelectionGeneticAlgorithm.cs

@@ -79 +79 @@
       get { return (ValueParameter<IntValue>)Parameters["Elites"]; }
     }
+    private IFixedValueParameter<BoolValue> ReevaluateElitesParameter {
+      get { return (IFixedValueParameter<BoolValue>)Parameters["ReevaluateElites"]; }
+    }
     private ValueParameter<IntValue> MaximumGenerationsParameter {
       get { return (ValueParameter<IntValue>)Parameters["MaximumGenerations"]; }
@@ -143 +146 @@
       get { return ElitesParameter.Value; }
       set { ElitesParameter.Value = value; }
+    }
+    public bool ReevaluteElites {
+      get { return ReevaluateElitesParameter.Value.Value; }
+      set { ReevaluateElitesParameter.Value.Value = value; }
     }
     public IntValue MaximumGenerations {
@@ -208 +215 @@
       if (successfulOffspringAnalyzer == null)
         successfulOffspringAnalyzer = new SuccessfulOffspringAnalyzer();
+      if (!Parameters.ContainsKey("ReevaluateElites")) {
+        Parameters.Add(new FixedValueParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)", (BoolValue)new BoolValue(false).AsReadOnly()) { Hidden = true });
+      }
       #endregion
 
@@ -232 +242 @@
       Parameters.Add(new OptionalConstrainedValueParameter<IManipulator>("Mutator", "The operator used to mutate solutions."));
       Parameters.Add(new ValueParameter<IntValue>("Elites", "The numer of elite solutions which are kept in each generation.", new IntValue(1)));
+      Parameters.Add(new FixedValueParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)", new BoolValue(false)) { Hidden = true });
       Parameters.Add(new ValueParameter<IntValue>("MaximumGenerations", "The maximum number of generations which should be processed.", new IntValue(1000)));
       Parameters.Add(new ValueLookupParameter<DoubleValue>("SuccessRatio", "The ratio of successful to total children that should be achieved.", new DoubleValue(1)));
@@ -274 +285 @@
       mainLoop.CrossoverParameter.ActualName = CrossoverParameter.Name;
       mainLoop.ElitesParameter.ActualName = ElitesParameter.Name;
+      mainLoop.ReevaluateElitesParameter.ActualName = ReevaluateElitesParameter.Name;
       mainLoop.EvaluatedSolutionsParameter.ActualName = "EvaluatedSolutions";
       mainLoop.MaximumGenerationsParameter.ActualName = MaximumGenerationsParameter.Name;

trunk/sources/HeuristicLab.Algorithms.OffspringSelectionGeneticAlgorithm/3.3/OffspringSelectionGeneticAlgorithmMainLoop.cs

@@ -63 +63 @@
       get { return (ValueLookupParameter<IntValue>)Parameters["Elites"]; }
     }
+    public IValueLookupParameter<BoolValue> ReevaluateElitesParameter {
+      get { return (IValueLookupParameter<BoolValue>)Parameters["ReevaluateElites"]; }
+    }
     public ValueLookupParameter<IntValue> MaximumGenerationsParameter {
       get { return (ValueLookupParameter<IntValue>)Parameters["MaximumGenerations"]; }
@@ -106 +109 @@
       : base() {
       Initialize();
+    }
+
+    [StorableHook(HookType.AfterDeserialization)]
+    private void AfterDeserialization() {
+      if (!Parameters.ContainsKey("ReevaluateElites")) {
+        Parameters.Add(new ValueLookupParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)"));
+      }
     }
 
@@ -120 +130 @@
       Parameters.Add(new ValueLookupParameter<IOperator>("Evaluator", "The operator used to evaluate solutions. This operator is executed in parallel, if an engine is used which supports parallelization."));
       Parameters.Add(new ValueLookupParameter<IntValue>("Elites", "The numer of elite solutions which are kept in each generation."));
+      Parameters.Add(new ValueLookupParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)"));
       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."));
@@ -170 +181 @@
       mainOperator.CurrentSuccessRatioParameter.ActualName = "CurrentSuccessRatio";
       mainOperator.ElitesParameter.ActualName = ElitesParameter.Name;
+      mainOperator.ReevaluateElitesParameter.ActualName = ReevaluateElitesParameter.Name;
       mainOperator.EvaluatedSolutionsParameter.ActualName = EvaluatedSolutionsParameter.Name;
       mainOperator.EvaluatorParameter.ActualName = EvaluatorParameter.Name;

trunk/sources/HeuristicLab.Algorithms.OffspringSelectionGeneticAlgorithm/3.3/OffspringSelectionGeneticAlgorithmMainOperator.cs

@@ -67 +67 @@
       get { return (ValueLookupParameter<IntValue>)Parameters["Elites"]; }
     }
+    public IValueLookupParameter<BoolValue> ReevaluateElitesParameter {
+      get { return (IValueLookupParameter<BoolValue>)Parameters["ReevaluateElites"]; }
+    }
     public LookupParameter<DoubleValue> ComparisonFactorParameter {
       get { return (LookupParameter<DoubleValue>)Parameters["ComparisonFactor"]; }
@@ -98 +101 @@
       : base() {
       Initialize();
+    }
+
+    [StorableHook(HookType.AfterDeserialization)]
+    private void AfterDeserialization() {
+      if (!Parameters.ContainsKey("ReevaluateElites")) {
+        Parameters.Add(new ValueLookupParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)"));
+      }
     }
 
@@ -112 +122 @@
       Parameters.Add(new LookupParameter<IntValue>("EvaluatedSolutions", "The number of evaluated solutions."));
       Parameters.Add(new ValueLookupParameter<IntValue>("Elites", "The numer of elite solutions which are kept in each generation."));
+      Parameters.Add(new ValueLookupParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)"));
       Parameters.Add(new LookupParameter<DoubleValue>("ComparisonFactor", "The comparison factor is used to determine whether the offspring should be compared to the better parent, the worse parent or a quality value linearly interpolated between them. It is in the range [0;1]."));
       Parameters.Add(new LookupParameter<DoubleValue>("CurrentSuccessRatio", "The current success ratio."));
@@ -159 +170 @@
       LeftReducer leftReducer = new LeftReducer();
       MergingReducer mergingReducer2 = new MergingReducer();
+      ConditionalBranch reevaluateElitesBranch = new ConditionalBranch();
+      UniformSubScopesProcessor uniformSubScopesProcessor7 = new UniformSubScopesProcessor();
+      Placeholder evaluator4 = new Placeholder();
+      SubScopesCounter subScopesCounter4 = new SubScopesCounter();
 
       selector.Name = "Selector (placeholder)";
@@ -253 +268 @@
       worstSelector.NumberOfSelectedSubScopesParameter.ActualName = ElitesParameter.Name;
       worstSelector.QualityParameter.ActualName = QualityParameter.Name;
+
+      reevaluateElitesBranch.ConditionParameter.ActualName = "ReevaluateElites";
+      reevaluateElitesBranch.Name = "Reevaluate elites ?";
+
+      uniformSubScopesProcessor7.Parallel.Value = true;
+
+      evaluator4.Name = "Evaluator (placeholder)";
+      evaluator4.OperatorParameter.ActualName = EvaluatorParameter.Name;
+
+      subScopesCounter4.Name = "Increment EvaluatedSolutions";
+      subScopesCounter4.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
       #endregion
 
@@ -310 +336 @@
       subScopesProcessor3.Successor = mergingReducer2;
       bestSelector.Successor = rightReducer;
-      rightReducer.Successor = null;
+      rightReducer.Successor = reevaluateElitesBranch;
+      reevaluateElitesBranch.TrueBranch = uniformSubScopesProcessor7;
+      uniformSubScopesProcessor7.Operator = evaluator4;
+      uniformSubScopesProcessor7.Successor = subScopesCounter4;
+      subScopesCounter4.Successor = null;
+      reevaluateElitesBranch.FalseBranch = null;
+      reevaluateElitesBranch.Successor = null;
       worstSelector.Successor = leftReducer;
       leftReducer.Successor = null;

trunk/sources/HeuristicLab.Algorithms.OffspringSelectionGeneticAlgorithm/3.3/SASEGASA.cs

@@ -85 +85 @@
       get { return (ValueParameter<IntValue>)Parameters["Elites"]; }
     }
+    private IFixedValueParameter<BoolValue> ReevaluateElitesParameter {
+      get { return (IFixedValueParameter<BoolValue>)Parameters["ReevaluateElites"]; }
+    }
     private ValueLookupParameter<DoubleValue> SuccessRatioParameter {
       get { return (ValueLookupParameter<DoubleValue>)Parameters["SuccessRatio"]; }
@@ -160 +163 @@
       get { return ElitesParameter.Value; }
       set { ElitesParameter.Value = value; }
+    }
+    public bool ReevaluteElites {
+      get { return ReevaluateElitesParameter.Value.Value; }
+      set { ReevaluateElitesParameter.Value.Value = value; }
     }
     public DoubleValue SuccessRatio {
@@ -236 +243 @@
       if (successfulOffspringAnalyzer == null)
         successfulOffspringAnalyzer = new SuccessfulOffspringAnalyzer();
+      if (!Parameters.ContainsKey("ReevaluateElites")) {
+        Parameters.Add(new FixedValueParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)", (BoolValue)new BoolValue(false).AsReadOnly()) { Hidden = true });
+      }
       #endregion
 
@@ -264 +274 @@
       Parameters.Add(new OptionalConstrainedValueParameter<IManipulator>("Mutator", "The operator used to mutate solutions."));
       Parameters.Add(new ValueParameter<IntValue>("Elites", "The numer of elite solutions which are kept in each generation.", new IntValue(1)));
+      Parameters.Add(new FixedValueParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)", new BoolValue(false)) { Hidden = true });
       Parameters.Add(new ValueLookupParameter<DoubleValue>("SuccessRatio", "The ratio of successful to total children that should be achieved.", new DoubleValue(1)));
       Parameters.Add(new ValueLookupParameter<DoubleValue>("ComparisonFactorLowerBound", "The lower bound of the comparison factor (start).", new DoubleValue(0.3)));
@@ -321 +332 @@
       mainLoop.CrossoverParameter.ActualName = CrossoverParameter.Name;
       mainLoop.ElitesParameter.ActualName = ElitesParameter.Name;
+      mainLoop.ReevaluateElitesParameter.ActualName = ReevaluateElitesParameter.Name;
       mainLoop.MutatorParameter.ActualName = MutatorParameter.Name;
       mainLoop.MutationProbabilityParameter.ActualName = MutationProbabilityParameter.Name;

trunk/sources/HeuristicLab.Algorithms.OffspringSelectionGeneticAlgorithm/3.3/SASEGASAMainLoop.cs

@@ -73 +73 @@
       get { return (ValueLookupParameter<IntValue>)Parameters["Elites"]; }
     }
+    public IValueLookupParameter<BoolValue> ReevaluateElitesParameter {
+      get { return (IValueLookupParameter<BoolValue>)Parameters["ReevaluateElites"]; }
+    }
     public ValueLookupParameter<ResultCollection> ResultsParameter {
       get { return (ValueLookupParameter<ResultCollection>)Parameters["Results"]; }
@@ -134 +137 @@
       Parameters.Add(new ValueLookupParameter<IOperator>("Evaluator", "The operator used to evaluate solutions. This operator is executed in parallel, if an engine is used which supports parallelization."));
       Parameters.Add(new ValueLookupParameter<IntValue>("Elites", "The numer of elite solutions which are kept in each generation."));
+      Parameters.Add(new ValueLookupParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)"));
       Parameters.Add(new ValueLookupParameter<ResultCollection>("Results", "The results collection to store the results."));
       Parameters.Add(new ValueLookupParameter<IOperator>("Analyzer", "The operator used to the analyze the villages."));
@@ -236 +240 @@
       mainOperator.CurrentSuccessRatioParameter.ActualName = "CurrentSuccessRatio";
       mainOperator.ElitesParameter.ActualName = ElitesParameter.Name;
+      mainOperator.ReevaluateElitesParameter.ActualName = ReevaluateElitesParameter.Name;
       mainOperator.EvaluatedSolutionsParameter.ActualName = EvaluatedSolutionsParameter.Name;
       mainOperator.EvaluatorParameter.ActualName = EvaluatorParameter.Name;
@@ -424 +429 @@
     }
 
+    [StorableHook(HookType.AfterDeserialization)]
+    private void AfterDeserialization() {
+      if (!Parameters.ContainsKey("ReevaluateElites")) {
+        Parameters.Add(new ValueLookupParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)"));
+      }
+    }
+
     public override IOperation Apply() {
       if (CrossoverParameter.ActualValue == null)