Changeset 5522 for branches/HeuristicLab.MetaOptimization/HeuristicLab.MetaOptimization.Test/Program.cs

Timestamp:

02/21/11 00:46:33 (13 years ago)

Author:

cneumuel

Message:

#1215

• implemented population diversity analysis

File:

• branches/HeuristicLab.MetaOptimization/HeuristicLab.MetaOptimization.Test/Program.cs (modified) (7 diffs)

branches/HeuristicLab.MetaOptimization/HeuristicLab.MetaOptimization.Test/Program.cs

@@ -23 +23 @@
 using HeuristicLab.Hive.ExperimentManager;
 using System.Threading.Tasks;
+using HeuristicLab.PluginInfrastructure.Manager;
+using System.Reflection;
 
 namespace HeuristicLab.MetaOptimization.Test {
   class Program {
+    static void Main(string[] args) {
+      //PluginLoader.pluginAssemblies.Any();
+      PluginManager pm = new PluginManager(Path.GetDirectoryName(Assembly.GetExecutingAssembly().Location));
+      pm.DiscoverAndCheckPlugins();
+
+      pm.Run(pm.Applications.Where(x => x.Name == "TestApp").SingleOrDefault());
+    }
+  }
+
+  [Plugin("TestPlugin", "1.0.0.0")]
+  [PluginFile("HeuristicLab.MetaOptimization.Test.exe", PluginFileType.Assembly)]
+  public class TestPlugin : PluginBase { }
+
+  [Application("TestApp")]
+  public class TestApp : ApplicationBase {
     //private static int metaAlgorithmPopulationSize = 50;
     //private static int metaAlgorithmMaxGenerations = 30;
@@ -31 +48 @@
     //private static int baseAlgorithmMaxGenerations = 1000;
 
-    private static int metaAlgorithmPopulationSize = 10;
-    private static int metaAlgorithmMaxGenerations = 10;
+    private static int metaAlgorithmPopulationSize = 20;
+    private static int metaAlgorithmMaxGenerations = 20;
     private static int metaProblemRepetitions = 2;
-    private static int baseAlgorithmMaxGenerations = 20;
+    private static int baseAlgorithmMaxGenerations = 10;
     private static double mutationProbability = 0.10;
 
-    static void Main(string[] args) {
+    public override void Run() {
       ContentManager.Initialize(new PersistenceContentManager());
-      
+
       //TestTableBuilder();
       //TestShorten();
 
+      //TestSimilarities(); return;
       //TestIntSampling();
       //TestDoubleSampling(); return;
@@ -51 +69 @@
       //TestCombinations3();
       //TestEnumeratorCollectionEnumerator();
-      TestCombinations4(); return;
+      //TestCombinations4(); return;
       //TestAlgorithmPerformanceIssue();
       //TestWaitAny();
@@ -59 +77 @@
       //TestItemDictionary();
 
-      
+
 
       MetaOptimizationProblem metaOptimizationProblem = new MetaOptimizationProblem();
@@ -87 +105 @@
     }
 
+    private static void TestSimilarities() {
+      Console.WriteLine("\nDoubleRange:");
+      var doubleRange = new DoubleValueRange(new DoubleValue(0), new DoubleValue(10), new DoubleValue(1));
+      var a = new DoubleValue(5.0);
+
+      for (double d = 0; d < 10; d += 0.1) {
+        var similarity = doubleRange.CalculateSimilarity(a, new DoubleValue(d));
+        Console.WriteLine("{0}: {1}", d, similarity);
+      }
+
+      Console.WriteLine("\nPecentRange:");
+      var percentRange = new PercentValueRange(new PercentValue(0), new PercentValue(1), new PercentValue(1));
+      var b = new PercentValue(0.05);
+
+      for (double d = 0; d < 1; d += 0.01) {
+        var similarity = percentRange.CalculateSimilarity(b, new PercentValue(d));
+        Console.WriteLine("{0}: {1}", d, similarity);
+      }
+
+      Console.WriteLine("\nIntRange:");
+      var intRange = new IntValueRange(new IntValue(50), new IntValue(100), new IntValue(1));
+      var c = new IntValue(90);
+
+      for (int i = 0; i < 100; i++) {
+        var similarity = intRange.CalculateSimilarity(c, new IntValue(i));
+        Console.WriteLine("{0}: {1}", i, similarity);
+      }
+
+      Console.WriteLine("\nValueConfigurations:");
+      var vc1 = SetupGAAlgorithm(typeof(GeneticAlgorithm), new MetaOptimizationProblem());
+      vc1.ParameterConfigurations.Single(x => x.Name == "Elites").Optimize = true;
+      vc1.ParameterConfigurations.Single(x => x.Name == "PopulationSize").Optimize = true;
+      vc1.ParameterConfigurations.Single(x => x.Name == "MutationProbability").Optimize = true;
+      vc1.ParameterConfigurations.Single(x => x.Name == "Selector").Optimize = true;
+
+      var vc2 = (IValueConfiguration)vc1.Clone();
+      Console.WriteLine("Assert(1): {0}", vc1.CalculateSimilarity(vc2));
+
+      ((IntValue)vc2.ParameterConfigurations.Single(x => x.Name == "PopulationSize").ValueConfigurations[0].ActualValue.Value).Value = 75;
+      Console.WriteLine("{0}", vc1.CalculateSimilarity(vc2));
+
+      ((PercentValue)vc2.ParameterConfigurations.Single(x => x.Name == "MutationProbability").ValueConfigurations[0].ActualValue.Value).Value = 0.15;
+      Console.WriteLine("{0}", vc1.CalculateSimilarity(vc2));
+
+      ((PercentValue)vc2.ParameterConfigurations.Single(x => x.Name == "MutationProbability").ValueConfigurations[0].ActualValue.Value).Value = 0.25;
+      Console.WriteLine("{0}", vc1.CalculateSimilarity(vc2));
+      ((PercentValue)vc2.ParameterConfigurations.Single(x => x.Name == "MutationProbability").ValueConfigurations[0].ActualValue.Value).Value = 0.35;
+      Console.WriteLine("{0}", vc1.CalculateSimilarity(vc2));
+      ((PercentValue)vc2.ParameterConfigurations.Single(x => x.Name == "MutationProbability").ValueConfigurations[0].ActualValue.Value).Value = 0.45;
+      Console.WriteLine("{0}", vc1.CalculateSimilarity(vc2));
+      ((PercentValue)vc2.ParameterConfigurations.Single(x => x.Name == "MutationProbability").ValueConfigurations[0].ActualValue.Value).Value = 0.55;
+      Console.WriteLine("{0}", vc1.CalculateSimilarity(vc2));
+
+      vc2.ParameterConfigurations.Single(x => x.Name == "Selector").ActualValueConfigurationIndex = 3;
+      Console.WriteLine("{0}", vc1.CalculateSimilarity(vc2));
+
+      var random = new Random.MersenneTwister(0);
+      for (int i = 0; i < 10; i++) {
+        vc2.Randomize(random);
+        Console.WriteLine("Randomized: {0}", vc1.CalculateSimilarity(vc2));
+      }
+    }
+
     private static void TestItemDictionary() {
       var dict = new ItemDictionary<StringValue, RunCollection>();
@@ -116 +197 @@
       using (var sw = new StreamWriter("normalCrossover-IntValue.txt")) {
         for (int i = 0; i < 10000; i++) {
-         sw.WriteLine(NormalIntValueCrossover.ApplyStatic(random, new IntValue(i1), new IntValue(i2), intRange));
+          sw.WriteLine(NormalIntValueCrossover.ApplyStatic(random, new IntValue(i1), new IntValue(i2), intRange));
         }
       }
@@ -557 +638 @@
         ProblemSize = new IntValue(2)
       });
-      metaOptimizationProblem.Problems.Add(new HeuristicLab.Problems.TestFunctions.SingleObjectiveTestFunctionProblem() {
-        Evaluator = new GriewankEvaluator(),
-        ProblemSize = new IntValue(20)
-      });
-      metaOptimizationProblem.Problems.Add(new HeuristicLab.Problems.TestFunctions.SingleObjectiveTestFunctionProblem() {
-        Evaluator = new GriewankEvaluator(),
-        ProblemSize = new IntValue(500)
-      });
+      //metaOptimizationProblem.Problems.Add(new HeuristicLab.Problems.TestFunctions.SingleObjectiveTestFunctionProblem() {
+      //  Evaluator = new GriewankEvaluator(),
+      //  ProblemSize = new IntValue(20)
+      //});
+      //metaOptimizationProblem.Problems.Add(new HeuristicLab.Problems.TestFunctions.SingleObjectiveTestFunctionProblem() {
+      //  Evaluator = new GriewankEvaluator(),
+      //  ProblemSize = new IntValue(500)
+      //});
 
       ConfigurePopulationSize(algorithmVc, 0, 20, 1);
-      //ConfigureMutationRate(algorithmVc, 0.0, 1.0, 0.01);
-      //ConfigureMutationOperator(algorithmVc);
+      ConfigureMutationRate(algorithmVc, 0.0, 1.0, 0.01);
+      ConfigureMutationOperator(algorithmVc);
       ConfigureElites(algorithmVc, 0, 30, 1);
-      //ConfigureSelectionOperator(algorithmVc, true);
+      ConfigureSelectionOperator(algorithmVc, true);
       return algorithmVc;
     }