Changeset 12005 for stable/HeuristicLab.Algorithms.ParameterlessPopulationPyramid/3.3/EvaluationTracker.cs

Timestamp:

02/13/15 15:00:15 (9 years ago)

Author:

abeham

Message:

#2174, #2282: merged revisions r11961,r11963,r11967,r11970,r11971,r11982,r11984,r11998,r12001,r12002,r12003,r12004,r11939,r11945,r11956,r11958,r11959,r11960,r11983,r11987,r11988,r11990,r11993,r11994,r11996,r11999,r12000 to stable

Location:

stable

Files:

• . (modified) (1 prop)
• HeuristicLab.Algorithms.ParameterlessPopulationPyramid (copied) (copied from trunk/sources/HeuristicLab.Algorithms.ParameterlessPopulationPyramid)
• HeuristicLab.Algorithms.ParameterlessPopulationPyramid/3.3/EvaluationTracker.cs (copied) (copied from trunk/sources/HeuristicLab.Algorithms.ParameterlessPopulationPyramid/3.3/EvaluationTracker.cs) (5 diffs)

stable/HeuristicLab.Algorithms.ParameterlessPopulationPyramid/3.3/EvaluationTracker.cs

@@ -22 +22 @@
 
 using System;
-using HeuristicLab.Problems.BinaryVector;
+using HeuristicLab.Common;
+using HeuristicLab.Core;
+using HeuristicLab.Data;
+using HeuristicLab.Encodings.BinaryVectorEncoding;
+using HeuristicLab.Parameters;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Problems.Binary;
 
 namespace HeuristicLab.Algorithms.ParameterlessPopulationPyramid {
@@ -28 +34 @@
   // B. W. Goldman and W. F. Punch, "Parameter-less Population Pyramid," GECCO, pp. 785–792, 2014
   // and the original source code in C++11 available from: https://github.com/brianwgoldman/Parameter-less_Population_Pyramid
-  public class EvaluationTracker : IBinaryVectorProblem {
-    private readonly IBinaryVectorProblem problem;
+  internal sealed class EvaluationTracker : BinaryProblem {
+    private readonly BinaryProblem problem;
 
     private int maxEvaluations;
@@ -49 +55 @@
     }
 
-    public bool[] BestSolution {
+    public BinaryVector BestSolution {
       get;
       private set;
@@ -55 +61 @@
     #endregion
 
-    public EvaluationTracker(IBinaryVectorProblem problem, int maxEvaluations) {
+    [StorableConstructor]
+    private EvaluationTracker(bool deserializing) : base(deserializing) { }
+    private EvaluationTracker(EvaluationTracker original, Cloner cloner)
+      : base(original, cloner) {
+      problem = cloner.Clone(original.problem);
+      maxEvaluations = original.maxEvaluations;
+      BestQuality = original.BestQuality;
+      Evaluations = original.Evaluations;
+      BestFoundOnEvaluation = original.BestFoundOnEvaluation;
+      BestSolution = cloner.Clone(BestSolution);
+    }
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new EvaluationTracker(this, cloner);
+    }
+    public EvaluationTracker(BinaryProblem problem, int maxEvaluations) {
       this.problem = problem;
       this.maxEvaluations = maxEvaluations;
-      BestSolution = new bool[0];
+      BestSolution = new BinaryVector(Length);
       BestQuality = double.NaN;
       Evaluations = 0;
       BestFoundOnEvaluation = 0;
+
+      if (Parameters.ContainsKey("Maximization")) Parameters.Remove("Maximization");
+      Parameters.Add(new FixedValueParameter<BoolValue>("Maximization", "Set to false if the problem should be minimized.", (BoolValue)new BoolValue(Maximization).AsReadOnly()) { Hidden = true });
     }
 
-    public double Evaluate(bool[] individual) {
+    public override double Evaluate(BinaryVector vector, IRandom random) {
       if (Evaluations >= maxEvaluations) throw new OperationCanceledException("Maximum Evaluation Limit Reached");
       Evaluations++;
-      double fitness = problem.Evaluate(individual);
+      double fitness = problem.Evaluate(vector, random);
       if (double.IsNaN(BestQuality) || problem.IsBetter(fitness, BestQuality)) {
         BestQuality = fitness;
-        BestSolution = (bool[])individual.Clone();
+        BestSolution = (BinaryVector)vector.Clone();
         BestFoundOnEvaluation = Evaluations;
       }
@@ -76 +99 @@
     }
 
-    #region ForwardedInteraface
-    public int Length {
+    public override int Length {
       get { return problem.Length; }
+      set { problem.Length = value; }
     }
-    public bool Maximization {
-      get { return problem.Maximization; }
+
+    public override bool Maximization {
+      get {
+        if (problem == null) return false;
+        return problem.Maximization;
+      }
     }
+
     public bool IsBetter(double quality, double bestQuality) {
       return problem.IsBetter(quality, bestQuality);
     }
-    #endregion
+
   }
 }