Changeset 16171 for branches/2943_MOBasicProblem_MOCMAES/HeuristicLab.Algorithms.MOCMAEvolutionStrategy

Timestamp:

09/21/18 09:18:49 (6 years ago)

Author:

bwerth

Message:

#2943 worked on MOBasicProblem - added Interfaces;reworked MOCalculators; several minor changes

Location:

branches/2943_MOBasicProblem_MOCMAES/HeuristicLab.Algorithms.MOCMAEvolutionStrategy/3.3

Files:

• HeuristicLab.Algorithms.MOCMAEvolutionStrategy-3.3.csproj (modified) (4 diffs)
• IIndicator.cs (modified) (1 diff)
• Indicators/CrowdingIndicator.cs (modified) (2 diffs)
• Indicators/HypervolumeIndicator.cs (modified) (3 diffs)
• Indicators/MinimalDistanceIndicator.cs (modified) (1 diff)
• MOCMAEvolutionStrategy.cs (modified) (9 diffs)

branches/2943_MOBasicProblem_MOCMAES/HeuristicLab.Algorithms.MOCMAEvolutionStrategy/3.3/HeuristicLab.Algorithms.MOCMAEvolutionStrategy-3.3.csproj

@@ -17 +17 @@
     <DebugType>full</DebugType>
     <Optimize>false</Optimize>
-    <OutputPath>$(SolutionDir)\bin\</OutputPath>
+    <OutputPath>..\..\..\..\trunk\bin\</OutputPath>
     <DefineConstants>DEBUG;TRACE</DefineConstants>
     <ErrorReport>prompt</ErrorReport>
@@ -80 +80 @@
       <HintPath>..\..\..\..\trunk\bin\HeuristicLab.ALGLIB-3.7.0.dll</HintPath>
     </Reference>
-    <Reference Include="HeuristicLab.Collections-3.3">
+    <Reference Include="HeuristicLab.Collections-3.3, Version=3.3.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL">
+      <SpecificVersion>False</SpecificVersion>
       <HintPath>..\..\..\..\trunk\bin\HeuristicLab.Collections-3.3.dll</HintPath>
     </Reference>
@@ -88 +89 @@
     <Reference Include="HeuristicLab.Core-3.3">
       <HintPath>..\..\..\..\trunk\bin\HeuristicLab.Core-3.3.dll</HintPath>
-    </Reference>
-    <Reference Include="HeuristicLab.Data-3.3">
-      <HintPath>..\..\..\..\trunk\bin\HeuristicLab.Data-3.3.dll</HintPath>
     </Reference>
     <Reference Include="HeuristicLab.Encodings.RealVectorEncoding-3.3">
@@ -131 +129 @@
       <Name>HeuristicLab.Analysis-3.3</Name>
     </ProjectReference>
+    <ProjectReference Include="..\..\HeuristicLab.Data\3.3\HeuristicLab.Data-3.3.csproj">
+      <Project>{bbab9df5-5ef3-4ba8-ade9-b36e82114937}</Project>
+      <Name>HeuristicLab.Data-3.3</Name>
+    </ProjectReference>
     <ProjectReference Include="..\..\HeuristicLab.Optimization\3.3\HeuristicLab.Optimization-3.3.csproj">
       <Project>{14ab8d24-25bc-400c-a846-4627aa945192}</Project>

branches/2943_MOBasicProblem_MOCMAES/HeuristicLab.Algorithms.MOCMAEvolutionStrategy/3.3/IIndicator.cs

@@ -34 +34 @@
     /// <param name="problem">The problem on which the front is evaluated (!! The function itself will NOT be evluated only bounds referencePoints & other metadata will be used</param>
     /// <returns>the index of the least contributing point according to any type of quality criteria</returns>
-    int LeastContributer(IReadOnlyList<Individual> front, MultiObjectiveBasicProblem<RealVectorEncoding> problem);
+    int LeastContributer(IReadOnlyList<Individual> front, IMultiObjectiveBasicProblem problem);
   }
 }

branches/2943_MOBasicProblem_MOCMAES/HeuristicLab.Algorithms.MOCMAEvolutionStrategy/3.3/Indicators/CrowdingIndicator.cs

@@ -28 +28 @@
 using HeuristicLab.Optimization;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Problems.TestFunctions.MultiObjective;
 
 namespace HeuristicLab.Algorithms.MOCMAEvolutionStrategy {
@@ -41 +42 @@
     #endregion
 
-    public int LeastContributer(IReadOnlyList<Individual> front, MultiObjectiveBasicProblem<RealVectorEncoding> problem) {
-      var bounds = problem.Encoding.Bounds;
+    public int LeastContributer(IReadOnlyList<Individual> front, IMultiObjectiveBasicProblem problem) {
       var extracted = front.Select(x => x.PenalizedFitness).ToArray();
       if (extracted.Length <= 2) return 0;
-      var pointsums = new double[extracted.Length];
-
-      for (var dim = 0; dim < problem.Maximization.Length; dim++) {
-        var arr = extracted.Select(x => x[dim]).ToArray();
-        Array.Sort(arr);
-        var fmax = problem.Encoding.Bounds[dim % bounds.Rows, 1];
-        var fmin = bounds[dim % bounds.Rows, 0];
-        var pointIdx = 0;
-        foreach (var point in extracted) {
-          var pos = Array.BinarySearch(arr, point[dim]);
-          var d = pos != 0 && pos != arr.Length - 1 ? (arr[pos + 1] - arr[pos - 1]) / (fmax - fmin) : double.PositiveInfinity;
-          pointsums[pointIdx] += d;
-          pointIdx++;
-        }
-      }
+      var pointsums = CrowdingCalculator.CalculateCrowdingDistances(extracted);
       return pointsums.Select((value, index) => new { value, index }).OrderBy(x => x.value).First().index;
     }

branches/2943_MOBasicProblem_MOCMAES/HeuristicLab.Algorithms.MOCMAEvolutionStrategy/3.3/Indicators/HypervolumeIndicator.cs

@@ -30 +30 @@
 using HeuristicLab.Problems.TestFunctions.MultiObjective;
 namespace HeuristicLab.Algorithms.MOCMAEvolutionStrategy {
-  [Item("HypervolumeIndicator", "Selection of Offspring based on contributing Hypervolume")]
+  [Item("HypervolumeIndicator", "Selection of offspring based on contributing Hypervolume")]
   [StorableClass]
   internal class HypervolumeIndicator : Item, IIndicator {
@@ -41 +41 @@
     #endregion
 
-    public int LeastContributer(IReadOnlyList<Individual> front, MultiObjectiveBasicProblem<RealVectorEncoding> problem) {
+    public int LeastContributer(IReadOnlyList<Individual> front, IMultiObjectiveBasicProblem problem) {
       var frontCopy = front.Select(x => x.PenalizedFitness).ToList();
       if (frontCopy.Count <= 1) return 0;
       var p = problem as MultiObjectiveTestFunctionProblem;
-      var refPoint = BuildReferencePoint(p != null ? frontCopy.Concat(new[] { p.ReferencePoint.CloneAsArray() }) : frontCopy, problem.Maximization);
+      var tep = p != null ? frontCopy.Concat(new[] {p.ReferencePoint.CloneAsArray()}) : frontCopy;
+      var refPoint = HypervolumeCalculator.CalculateNadirPoint(tep, problem.Maximization);
       var contributions = Enumerable.Range(0, frontCopy.Count).Select(i => Contribution(frontCopy, i, problem.Maximization, refPoint));
       return contributions.Select((value, index) => new { value, index }).OrderBy(x => x.value).First().index;
@@ -54 +55 @@
       var point = front[idx];
       front.RemoveAt(idx);
-      var contribution = -Hypervolume.Calculate(front.ToArray(), refPoint, maximization);
+      var contribution = -HypervolumeCalculator.CalculateHypervolume(front.ToArray(), refPoint, maximization);
       front.Insert(idx, point);
       return contribution;
-    }
-    private static double[] BuildReferencePoint(IEnumerable<double[]> front, IReadOnlyList<bool> maximization) {
-      var refPoint = new double[maximization.Count];
-      foreach (var point in front)
-        for (var i = 0; i < maximization.Count; i++)
-          refPoint[i] = maximization[i] ? Math.Min(refPoint[i], point[i]) : Math.Max(refPoint[i], point[i]);
-      return refPoint;
     }
     #endregion

branches/2943_MOBasicProblem_MOCMAES/HeuristicLab.Algorithms.MOCMAEvolutionStrategy/3.3/Indicators/MinimalDistanceIndicator.cs

@@ -42 +42 @@
     #endregion
 
-    public int LeastContributer(IReadOnlyList<Individual> front, MultiObjectiveBasicProblem<RealVectorEncoding> problem) {
+    public int LeastContributer(IReadOnlyList<Individual> front, IMultiObjectiveBasicProblem problem) {
       var extracted = front.Select(x => x.PenalizedFitness).ToArray();
       if (extracted.Length <= 2) return 0;

branches/2943_MOBasicProblem_MOCMAES/HeuristicLab.Algorithms.MOCMAEvolutionStrategy/3.3/MOCMAEvolutionStrategy.cs

@@ -42 +42 @@
   public class MOCMAEvolutionStrategy : BasicAlgorithm {
     public override Type ProblemType {
-      get { return typeof(MultiObjectiveBasicProblem<RealVectorEncoding>); }
-    }
-    public new MultiObjectiveBasicProblem<RealVectorEncoding> Problem {
-      get { return (MultiObjectiveBasicProblem<RealVectorEncoding>)base.Problem; }
+      get { return typeof(IMultiObjectiveBasicProblem); }
+    }
+    public new IMultiObjectiveBasicProblem Problem {
+      get { return (IMultiObjectiveBasicProblem)base.Problem; }
       set { base.Problem = value; }
     }
     public override bool SupportsPause {
       get { return true; }
+    }
+
+    private RealVectorEncoding Encoding {
+      get {
+        if(Problem == null || !(Problem.Encoding is RealVectorEncoding))
+          throw new ArgumentException("Multiobjective CMA-ES is only applicable to problems with RealVectorEncodings");
+        return (RealVectorEncoding)Problem.Encoding;
+      }
     }
 
@@ -309 +317 @@
       solutions = new Individual[PopulationSize];
       for (var i = 0; i < PopulationSize; i++) {
-        var x = new RealVector(Problem.Encoding.Length); // Uniform distibution in all dimensions assumed.
-        var bounds = Problem.Encoding.Bounds;
-        for (var j = 0; j < Problem.Encoding.Length; j++) {
+        var x = new RealVector(Encoding.Length); // Uniform distibution in all dimensions assumed.
+        var bounds = Encoding.Bounds;
+        for (var j = 0; j < Encoding.Length; j++) {
           var dim = j % bounds.Rows;
           x[j] = random.NextDouble() * (bounds[dim, 1] - bounds[dim, 0]) + bounds[dim, 0];
@@ -322 +330 @@
     private void InitStrategy() {
       const int lambda = 1;
-      double n = Problem.Encoding.Length;
+      double n = Encoding.Length;
       targetSuccessProbability = 1.0 / (5.0 + Math.Sqrt(lambda) / 2.0);
       stepSizeDampeningFactor = 1.0 + n / (2.0 * lambda);
@@ -358 +366 @@
       if (problem == null) return;
       if (problem.BestKnownFront != null) {
-        ResultsBestKnownHypervolume = Hypervolume.Calculate(problem.BestKnownFront.ToJaggedArray(), problem.TestFunction.ReferencePoint(problem.Objectives), Problem.Maximization);
+        ResultsBestKnownHypervolume = HypervolumeCalculator.CalculateHypervolume(problem.BestKnownFront.ToJaggedArray(), problem.TestFunction.ReferencePoint(problem.Objectives), Problem.Maximization);
         ResultsDifferenceBestKnownHypervolume = ResultsBestKnownHypervolume;
       }
@@ -417 +425 @@
     }
     private RealVector ClosestFeasible(RealVector x) {
-      var bounds = Problem.Encoding.Bounds;
+      var bounds = Encoding.Bounds;
       var r = new RealVector(x.Length);
       for (var i = 0; i < x.Length; i++) {
@@ -426 +434 @@
     }
     private bool IsFeasable(RealVector offspring) {
-      var bounds = Problem.Encoding.Bounds;
+      var bounds = Encoding.Bounds;
       for (var i = 0; i < offspring.Length; i++) {
         var dim = i % bounds.Rows;
@@ -438 +446 @@
       //perform a nondominated sort to assign the rank to every element
       int[] ranks;
-      var fronts = DominationCalculator<Individual>.CalculateAllParetoFronts(parents.ToArray(), parents.Select(i => i.PenalizedFitness).ToArray(), Problem.Maximization, out ranks);
+      var fronts = DominationCalculator.CalculateAllParetoFronts(parents.ToArray(), parents.Select(i => i.PenalizedFitness).ToArray(), Problem.Maximization, out ranks);
 
       //deselect the highest rank fronts until we would end up with less or equal mu elements
@@ -470 +478 @@
 
     private void Analyze() {
-      ResultsScatterPlot = new ParetoFrontScatterPlot(solutions.Select(x => x.Fitness).ToArray(), solutions.Select(x => x.Mean.ToArray()).ToArray(), ResultsScatterPlot.ParetoFront, ResultsScatterPlot.Objectives, ResultsScatterPlot.ProblemSize);
+      var qualities = solutions.Select(x => x.Fitness).ToArray();
+
+      //to do check for side effects
+      ResultsScatterPlot = new ParetoFrontScatterPlot(qualities, solutions.Select(x => x.Mean.ToArray()).ToArray(), ResultsScatterPlot.ParetoFront, ResultsScatterPlot.Objectives, ResultsScatterPlot.ProblemSize);
       ResultsSolutions = solutions.Select(x => x.Mean.ToArray()).ToMatrix();
 
@@ -476 +487 @@
       if (problem == null) return;
 
-      var front = NonDominatedSelect.GetDominatingVectors(solutions.Select(x => x.Fitness), problem.ReferencePoint.CloneAsArray(), Problem.Maximization, true).ToArray();
-      if (front.Length == 0) return;
-      var bounds = problem.Bounds.CloneAsMatrix();
-      ResultsCrowding = Crowding.Calculate(front, bounds);
-      ResultsSpacing = Spacing.Calculate(front);
-      ResultsGenerationalDistance = problem.BestKnownFront != null ? GenerationalDistance.Calculate(front, problem.BestKnownFront.ToJaggedArray(), 1) : double.NaN;
-      ResultsInvertedGenerationalDistance = problem.BestKnownFront != null ? InvertedGenerationalDistance.Calculate(front, problem.BestKnownFront.ToJaggedArray(), 1) : double.NaN;
-      ResultsHypervolume = Hypervolume.Calculate(front, problem.ReferencePoint.CloneAsArray(), Problem.Maximization);
+      
+      if (qualities.Length == 0) return;
+      ResultsCrowding = CrowdingCalculator.CalculateCrowding(qualities);
+      ResultsSpacing = SpacingCalculator.CalculateSpacing(qualities);
+      ResultsGenerationalDistance = problem.BestKnownFront != null ? GenerationalDistanceCalculator.CalculateGenerationalDistance(qualities, problem.BestKnownFront.ToJaggedArray(), 1) : double.NaN;
+      ResultsInvertedGenerationalDistance = problem.BestKnownFront != null ? GenerationalDistanceCalculator.CalculateInverseGenerationalDistance(qualities, problem.BestKnownFront.ToJaggedArray(), 1) : double.NaN;
+      ResultsHypervolume = HypervolumeCalculator.CalculateHypervolume(qualities, problem.ReferencePoint.CloneAsArray(), Problem.Maximization);
       ResultsBestHypervolume = Math.Max(ResultsHypervolume, ResultsBestHypervolume);
       ResultsDifferenceBestKnownHypervolume = ResultsBestKnownHypervolume - ResultsBestHypervolume;