Changeset 13339 for branches/ProblemRefactoring/HeuristicLab.Algorithms.ParameterlessPopulationPyramid

Timestamp:

11/23/15 16:14:57 (9 years ago)

Author:

mkommend

Message:

#2521: Rectored problems and encodings.

Location:

branches/ProblemRefactoring/HeuristicLab.Algorithms.ParameterlessPopulationPyramid/3.3

Files:

• EvaluationTracker.cs (modified) (5 diffs)
• HeuristicLab.Algorithms.ParameterlessPopulationPyramid-3.3.csproj (modified) (1 diff)
• HillClimber.cs (modified) (4 diffs)
• LinkageCrossover.cs (modified) (3 diffs)
• ParameterlessPopulationPyramid.cs (modified) (5 diffs)

branches/ProblemRefactoring/HeuristicLab.Algorithms.ParameterlessPopulationPyramid/3.3/EvaluationTracker.cs

@@ -26 +26 @@
 using HeuristicLab.Data;
 using HeuristicLab.Encodings.BinaryVectorEncoding;
+using HeuristicLab.Optimization;
 using HeuristicLab.Parameters;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
-using HeuristicLab.Problems.Binary;
 
 namespace HeuristicLab.Algorithms.ParameterlessPopulationPyramid {
@@ -34 +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
-  internal sealed class EvaluationTracker : BinaryProblem {
-    private readonly BinaryProblem problem;
+  internal sealed class EvaluationTracker : SingleObjectiveProblem<BinaryVectorEncoding, BinaryVector> {
+    private readonly ISingleObjectiveProblem<BinaryVectorEncoding, BinaryVector> problem;
 
     private int maxEvaluations;
@@ -59 +59 @@
       private set;
     }
+
+    public new BinaryVectorEncoding Encoding {
+      get { return problem.Encoding; }
+    }
     #endregion
 
@@ -75 +79 @@
       return new EvaluationTracker(this, cloner);
     }
-    public EvaluationTracker(BinaryProblem problem, int maxEvaluations) {
+    public EvaluationTracker(ISingleObjectiveProblem<BinaryVectorEncoding, BinaryVector> problem, int maxEvaluations) {
       this.problem = problem;
       this.maxEvaluations = maxEvaluations;
-      BestSolution = new BinaryVector(Length);
+      BestSolution = new BinaryVector(problem.Encoding.Length);
       BestQuality = double.NaN;
       Evaluations = 0;
@@ -99 +103 @@
     }
 
-    public override int Length {
-      get { return problem.Length; }
-      set { problem.Length = value; }
-    }
-
     public override bool Maximization {
       get {

branches/ProblemRefactoring/HeuristicLab.Algorithms.ParameterlessPopulationPyramid/3.3/HeuristicLab.Algorithms.ParameterlessPopulationPyramid-3.3.csproj

@@ -148 +148 @@
       <Private>False</Private>
     </ProjectReference>
-    <ProjectReference Include="..\..\HeuristicLab.Problems.Binary\3.3\HeuristicLab.Problems.Binary-3.3.csproj">
-      <Project>{fc627be5-0f93-47d8-bd2e-530ea2b8aa5f}</Project>
-      <Name>HeuristicLab.Problems.Binary-3.3</Name>
-      <Private>False</Private>
-    </ProjectReference>
     <ProjectReference Include="..\..\HeuristicLab.Random\3.3\HeuristicLab.Random-3.3.csproj">
       <Project>{f4539fb6-4708-40c9-be64-0a1390aea197}</Project>

branches/ProblemRefactoring/HeuristicLab.Algorithms.ParameterlessPopulationPyramid/3.3/HillClimber.cs

@@ -32 +32 @@
 using HeuristicLab.Parameters;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
-using HeuristicLab.Problems.Binary;
 using HeuristicLab.Random;
 
@@ -50 +49 @@
 
     public override Type ProblemType {
-      get { return typeof(BinaryProblem); }
+      get { return typeof(ISingleObjectiveProblem<BinaryVectorEncoding, BinaryVector>); }
     }
-    public new BinaryProblem Problem {
-      get { return (BinaryProblem)base.Problem; }
+    public new ISingleObjectiveProblem<BinaryVectorEncoding, BinaryVector> Problem {
+      get { return (ISingleObjectiveProblem<BinaryVectorEncoding, BinaryVector>)base.Problem; }
       set { base.Problem = value; }
     }
@@ -84 +83 @@
       Results.Add(new Result("Best quality", BestQuality));
       for (int iteration = 0; iteration < Iterations; iteration++) {
-        var solution = new BinaryVector(Problem.Length);
+        var solution = new BinaryVector(Problem.Encoding.Length);
         for (int i = 0; i < solution.Length; i++) {
           solution[i] = random.Next(2) == 1;
@@ -98 +97 @@
     }
     // In the GECCO paper, Section 2.1
-    public static double ImproveToLocalOptimum(BinaryProblem problem, BinaryVector solution, double fitness, IRandom rand) {
+    public static double ImproveToLocalOptimum(ISingleObjectiveProblem<BinaryVectorEncoding, BinaryVector> problem, BinaryVector solution, double fitness, IRandom rand) {
       var tried = new HashSet<int>();
       do {

branches/ProblemRefactoring/HeuristicLab.Algorithms.ParameterlessPopulationPyramid/3.3/LinkageCrossover.cs

@@ -24 +24 @@
 using HeuristicLab.Core;
 using HeuristicLab.Encodings.BinaryVectorEncoding;
-using HeuristicLab.Problems.Binary;
+using HeuristicLab.Optimization;
 using HeuristicLab.Random;
 
@@ -33 +33 @@
   public static class LinkageCrossover {
     // In the GECCO paper, Figure 3
-    public static double ImproveUsingTree(LinkageTree tree, IList<BinaryVector> donors, BinaryVector solution, double fitness, BinaryProblem problem, IRandom rand) {
+    public static double ImproveUsingTree(LinkageTree tree, IList<BinaryVector> donors, BinaryVector solution, double fitness, ISingleObjectiveProblem<BinaryVectorEncoding, BinaryVector> problem, IRandom rand) {
       var options = Enumerable.Range(0, donors.Count).ToArray();
       foreach (var cluster in tree.Clusters) {
@@ -48 +48 @@
     }
 
-    private static double Donate(BinaryVector solution, double fitness, BinaryVector source, IEnumerable<int> cluster, BinaryProblem problem, IRandom rand, out bool changed) {
+    private static double Donate(BinaryVector solution, double fitness, BinaryVector source, IEnumerable<int> cluster, ISingleObjectiveProblem<BinaryVectorEncoding, BinaryVector> problem, IRandom rand, out bool changed) {
       // keep track of which bits flipped to make the donation
       List<int> flipped = new List<int>();

branches/ProblemRefactoring/HeuristicLab.Algorithms.ParameterlessPopulationPyramid/3.3/ParameterlessPopulationPyramid.cs

@@ -32 +32 @@
 using HeuristicLab.Parameters;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
-using HeuristicLab.Problems.Binary;
 using HeuristicLab.Random;
 
@@ -44 +43 @@
   public class ParameterlessPopulationPyramid : BasicAlgorithm {
     public override Type ProblemType {
-      get { return typeof(BinaryProblem); }
-    }
-    public new BinaryProblem Problem {
-      get { return (BinaryProblem)base.Problem; }
+      get { return typeof(ISingleObjectiveProblem<BinaryVectorEncoding, BinaryVector>); }
+    }
+    public new ISingleObjectiveProblem<BinaryVectorEncoding, BinaryVector> Problem {
+      get { return (ISingleObjectiveProblem<BinaryVectorEncoding, BinaryVector>)base.Problem; }
       set { base.Problem = value; }
     }
@@ -183 +182 @@
       if (seen.Contains(solution)) return;
       if (level == pyramid.Count) {
-        pyramid.Add(new Population(tracker.Length, random));
+        pyramid.Add(new Population(tracker.Encoding.Length, random));
       }
       var copied = (BinaryVector)solution.Clone();
@@ -193 +192 @@
     private double iterate() {
       // Create a random solution
-      BinaryVector solution = new BinaryVector(tracker.Length);
+      BinaryVector solution = new BinaryVector(tracker.Encoding.Length);
       for (int i = 0; i < solution.Length; i++) {
         solution[i] = random.Next(2) == 1;
@@ -249 +248 @@
           fitness = iterate();
           cancellationToken.ThrowIfCancellationRequested();
-        } finally {
+        }
+        finally {
           ResultsEvaluations = tracker.Evaluations;
           ResultsBestSolution = new BinaryVector(tracker.BestSolution);