Changeset 12005 for stable/HeuristicLab.Algorithms.ParameterlessPopulationPyramid/3.3/HillClimber.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/HillClimber.cs (modified) (6 diffs)

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

@@ -28 +28 @@
 using HeuristicLab.Core;
 using HeuristicLab.Data;
+using HeuristicLab.Encodings.BinaryVectorEncoding;
 using HeuristicLab.Optimization;
 using HeuristicLab.Parameters;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Problems.Binary;
 using HeuristicLab.Random;
 
@@ -38 +40 @@
   // 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
-  [Item("Hill Climber", "Test algorithm.")]
+  [Item("Hill Climber", "Binary Hill Climber.")]
   [StorableClass]
-  [Creatable("Parameterless Population Pyramid")]
+  [Creatable("Algorithms")]
   public class HillClimber : BasicAlgorithm {
     [Storable]
@@ -48 +50 @@
 
     public override Type ProblemType {
-      get { return typeof(BinaryVectorProblem); }
+      get { return typeof(BinaryProblem); }
     }
-    public new BinaryVectorProblem Problem {
-      get { return (BinaryVectorProblem)base.Problem; }
+    public new BinaryProblem Problem {
+      get { return (BinaryProblem)base.Problem; }
       set { base.Problem = value; }
     }
@@ -82 +84 @@
       Results.Add(new Result("Best quality", BestQuality));
       for (int iteration = 0; iteration < Iterations; iteration++) {
-        bool[] solution = new bool[Problem.Length];
+        var solution = new BinaryVector(Problem.Length);
         for (int i = 0; i < solution.Length; i++) {
           solution[i] = random.Next(2) == 1;
         }
 
-        var fitness = Problem.Evaluate(solution);
+        var fitness = Problem.Evaluate(solution, random);
 
         fitness = ImproveToLocalOptimum(Problem, solution, fitness, random);
@@ -96 +98 @@
     }
     // In the GECCO paper, Section 2.1
-    public static double ImproveToLocalOptimum(IBinaryVectorProblem problem, bool[] solution, double fitness, IRandom rand) {
+    public static double ImproveToLocalOptimum(BinaryProblem problem, BinaryVector solution, double fitness, IRandom rand) {
       var tried = new HashSet<int>();
       do {
@@ -103 +105 @@
           if (tried.Contains(option)) continue;
           solution[option] = !solution[option];
-          double newFitness = problem.Evaluate(solution);
+          double newFitness = problem.Evaluate(solution, rand);
           if (problem.IsBetter(newFitness, fitness)) {
             fitness = newFitness;