Changeset 5293 for branches/HeuristicLab.MetaOptimization/HeuristicLab.MetaOptimization.Test

Timestamp:

01/14/11 00:41:58 (13 years ago)

Author:

cneumuel

Message:

#1215

• added NormalCrossover operators
• renamed MedianQualityAnalyzer to ReferenceQualityAnalyzer

File:

• branches/HeuristicLab.MetaOptimization/HeuristicLab.MetaOptimization.Test/Program.cs (modified) (5 diffs)

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

@@ -33 +33 @@
     private static int metaAlgorithmPopulationSize = 7;
     private static int metaAlgorithmMaxGenerations = 20;
-    private static int metaProblemRepetitions = 3;
+    private static int metaProblemRepetitions = 6;
     private static int baseAlgorithmMaxGenerations = 50;
     private static double mutationProbability = 0.35;
@@ -56 +56 @@
       //TestExecutionTimeUpdateInvervalPerformance();
       //TestMemoryConsumption();
+      //TestNormalCrossover();
 
       GeneticAlgorithm baseLevelAlgorithm = new GeneticAlgorithm();
@@ -83 +84 @@
 
       Console.ReadLine();
+    }
+
+    private static void TestNormalCrossover() {
+      var random = new MersenneTwister();
+      double d1 = 0.5;
+      double d2 = 0.6;
+      var doubleRange = new DoubleValueRange(new DoubleValue(0.0), new DoubleValue(1.0), new DoubleValue(0.01));
+
+      using (var sw = new StreamWriter("normalCrossover-DoubleValue.txt")) {
+        for (int i = 0; i < 10000; i++) {
+          sw.WriteLine(NormalDoubleValueCrossover.ApplyStatic(random, new DoubleValue(d1), new DoubleValue(d2), doubleRange));
+        }
+      }
+
+      int i1 = 180;
+      int i2 = 160;
+      var intRange = new IntValueRange(new IntValue(100), new IntValue(200), new IntValue(1));
+
+      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));
+        }
+      }
     }
 
@@ -510 +534 @@
       ParameterConfigurationTree algorithmVc = metaOptimizationProblem.ParameterConfigurationTree;
 
-      metaOptimizationProblem.Problems.Add(new HeuristicLab.Problems.TestFunctions.SingleObjectiveTestFunctionProblem() {
-        Evaluator = new GriewankEvaluator(),
-        ProblemSize = new IntValue(5)
-      });
+      //metaOptimizationProblem.Problems.Add(new HeuristicLab.Problems.TestFunctions.SingleObjectiveTestFunctionProblem() {
+      //  Evaluator = new GriewankEvaluator(),
+      //  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(50)
       });
-      metaOptimizationProblem.Problems.Add(new HeuristicLab.Problems.TestFunctions.SingleObjectiveTestFunctionProblem() {
-        Evaluator = new GriewankEvaluator(),
-        ProblemSize = new IntValue(500)
-      });
 
       ConfigurePopulationSize(algorithmVc, 12, 100, 1);
-      ConfigureMutationRate(algorithmVc, 0.0, 1.0, 0.01);
+      ConfigureMutationRate(algorithmVc, 0.0, 1.0, 0.0001);
       ConfigureMutationOperator(algorithmVc);
-      //ConfigureElites(algorithmVc, 0, 10, 1);
+      ConfigureElites(algorithmVc, 0, 10, 1);
       //ConfigureSelectionOperator(algorithmVc, true);
       return algorithmVc;
@@ -738 +762 @@
 
                   tb.AppendRow(
-                    ((DoubleValue)run.Results["AverageQualityNormalized"]).Value.ToString("#0.00"),
+                    ((DoubleValue)run.Results["AverageQualityNormalized"]).Value.ToString("#0.0000"),
                     ((DoubleArray)run.Results["RunsAverageQualities"]).ToString(),
                     ((IntValue)run.Parameters["PopulationSize"]).Value.ToString(),
-                    ((DoubleValue)run.Parameters["MutationProbability"]).Value.ToString("0.00"),
+                    ((DoubleValue)run.Parameters["MutationProbability"]).Value.ToString("0.0000"),
                     ((IntValue)run.Parameters["Elites"]).Value.ToString(),
                     Shorten(selector, 20),