Changeset 15289 for branches/PushGP/HeuristicLab.PushGP/HeuristicLab.Problems.ProgramSynthesis/Push/Crossover

Timestamp:

07/26/17 19:34:13 (7 years ago)

Author:

pkimmesw

Message:

#2665 Fixed analyzer, fixed Plush encoding + operators, adpated print evaluation according to McPhee

Location:

branches/PushGP/HeuristicLab.PushGP

Files:

• . (modified) (1 prop)
• HeuristicLab.Problems.ProgramSynthesis/Push/Crossover/AlternationCrossover.cs (modified) (4 diffs)

branches/PushGP/HeuristicLab.PushGP/HeuristicLab.Problems.ProgramSynthesis/Push/Crossover/AlternationCrossover.cs

@@ -10 +10 @@
   using HeuristicLab.Parameters;
   using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+  using HeuristicLab.Problems.ProgramSynthesis.Base.Extensions;
   using HeuristicLab.Problems.ProgramSynthesis.Push.Encoding;
   using HeuristicLab.Random;
@@ -23 +24 @@
     public AlternationCrossover() {
       Parameters.Add(new LookupParameter<IRandom>("Random", "The pseudo random number generator which should be used for stochastic crossover operators."));
+
       Parameters.Add(new ScopeTreeLookupParameter<PlushVector>("Parents", "The parent vectors which should be crossed."));
       ParentsParameter.ActualName = "PlushVector";
+
       Parameters.Add(new LookupParameter<PlushVector>("Child", "The child vector resulting from the crossover."));
       ChildParameter.ActualName = "PlushVector";
+
       Parameters.Add(new FixedValueParameter<PercentValue>("AlternationRate", "Specifies the probability of switching to another parent.", new PercentValue(0.5)));
       Parameters.Add(new FixedValueParameter<DoubleValue>("AlignmentDeviation", "When alternating between parents, the index at which to continue copying may be offset backward or forward some amount based on a random sample from a normal distribution with mean 0 and standard deviation set by the alignment deviation parameter", new DoubleValue(1.0)));
@@ -90 +94 @@
 
     public sealed override IOperation InstrumentedApply() {
-      ChildParameter.ActualValue = Cross(RandomParameter.ActualValue, ParentsParameter.ActualValue);
+      ChildParameter.ActualValue = Cross(
+        RandomParameter.ActualValue,
+        ParentsParameter.ActualValue,
+        AlternationRate,
+        MaxLength,
+        AlignmentDeviation);
       return base.InstrumentedApply();
     }
 
-    private PlushVector Cross(IRandom random, ItemArray<PlushVector> parents) {
-      var normalDistributedRandom = new NormalDistributedRandom(random, Mean, AlignmentDeviation);
+    private static PlushVector Cross(
+      IRandom random,
+      ItemArray<PlushVector> parents,
+      double alternationRate,
+      int maxChildLength,
+      double alignmentDeviation) {
+      var normalDistributedRandom = new NormalDistributedRandom(random, Mean, alignmentDeviation);
       var maxLength = parents.Max(p => p.Entries.Count);
       var parentIndex = random.Next(0, 2);
       var parent = parents[parentIndex];
       var child = new PlushVector(maxLength);
-      var maxChildLength = MaxLength;
 
       for (var i = 0; i < maxLength && child.Entries.Count <= maxChildLength; i++) {
@@ -109 +122 @@
 
         // switch parent?
-        if (random.NextDouble() < AlternationRate) {
+        if (random.NextDouble() < alternationRate) {
           parentIndex = parentIndex == 0 ? 1 : 0;
           parent = parents[parentIndex];
-          i += normalDistributedRandom.Next();
+          i += normalDistributedRandom.NextRounded();
           i = Math.Max(i, 0);
         }