Changeset 15275 for branches/PushGP/HeuristicLab.PushGP/HeuristicLab.Problems.ProgramSynthesis/Push/Selector

Timestamp:

07/20/17 11:39:53 (8 years ago)

Author:

pkimmesw

Message:

#2665 Added PlushEncoding, ZeroErrorDistributionAnalzer

File:

• branches/PushGP/HeuristicLab.PushGP/HeuristicLab.Problems.ProgramSynthesis/Push/Selector/LexicaseSelector.cs (modified) (5 diffs)

branches/PushGP/HeuristicLab.PushGP/HeuristicLab.Problems.ProgramSynthesis/Push/Selector/LexicaseSelector.cs

@@ -37 +37 @@
   /// <summary>
   /// A lexicase selection operator which considers all successful evaluated training cases for selection.
-  ///
-  /// ToDo: LexicaseSelector and ICaseSingleObjectiveSelector are ISingleObjectiveOperator, which contains Maximization and Qualities which is not needed
   /// </summary>
   [Item("LexicaseSelector", "A lexicase selection operator which considers all successful evaluated training cases for selection.")]
@@ -45 +43 @@
     public ILookupParameter<ItemArray<DoubleArray>> CaseQualitiesParameter
     {
-      get { return (ILookupParameter<ItemArray<DoubleArray>>)Parameters[PushProblem.CaseQualitiesScopeParameterName]; }
+      get { return (ILookupParameter<ItemArray<DoubleArray>>)Parameters[IntegerVectorPushProblem.CaseQualitiesScopeParameterName]; }
     }
 
@@ -57 +55 @@
     public LexicaseSelector() {
       Parameters.Add(new ScopeTreeLookupParameter<DoubleArray>(
-        PushProblem.CaseQualitiesScopeParameterName,
+        IntegerVectorPushProblem.CaseQualitiesScopeParameterName,
         "The quality of every single training case for each individual."));
     }
@@ -68 +66 @@
       var selected = new IScope[count];
       var caseQualities = CaseQualitiesParameter.ActualValue;
-      var repeats = Math.Ceiling(count / (double)population.Count);
+      var repeats = (int)Math.Ceiling(count / (double)population.Count);
       var caseCount = caseQualities[0].Length;
       var source = Enumerable.Range(0, population.Count).ToList();
@@ -77 +75 @@
 
         // copy list if required
-        var pool = k == repeats - 1 ? source : new List<int>(source);
+        var pool = repeats == 1 ? source : new List<int>(source);
         var countLimit = Math.Min(count - k * population.Count, population.Count);
 
         for (var i = 0; i < countLimit; i++) {
-          var bestIndividuals = pool;
+          var candidates = pool;
 
-          for (var j = 0; j < fitnessCaseIndexes.Count && bestIndividuals.Count > 1; j++)
-            bestIndividuals = GetBestIndividuals(maximization, caseQualities, bestIndividuals, fitnessCaseIndexes[j]);
+          for (var j = 0; j < fitnessCaseIndexes.Count && candidates.Count > 1; j++) {
+            candidates = GetBestIndividuals(maximization, caseQualities, candidates, fitnessCaseIndexes[j]);
+          }
 
           /*  If only one individual remains, it is the chosen parent. If no more fitness cases are left, a parent is
               chosen randomly from the remaining individuals */
-          var bestIndividualIndex = bestIndividuals.Count == 1 ? bestIndividuals[0] : bestIndividuals.Random(random);
+          var bestIndividualIndex = candidates.Count == 1 ? candidates[0] : candidates.Random(random);
           var bestIndividual = population[bestIndividualIndex];