Changeset 17693 for branches/2825-NSGA3

Timestamp:

07/22/20 20:07:22 (5 years ago)

Author:

dleko

Message:

#2825 Remove unnecessary parameter. Refactoring.

Location:

branches/2825-NSGA3/HeuristicLab.Algorithms.NSGA3/3.3

Files:

• NSGA3.cs (modified) (16 diffs)
• ReferencePoint.cs (modified) (3 diffs)

branches/2825-NSGA3/HeuristicLab.Algorithms.NSGA3/3.3/NSGA3.cs

@@ -26 +26 @@
     public class NSGA3 : BasicAlgorithm
     {
-        public override bool SupportsPause => false;
+        public override bool SupportsPause => true;
 
         #region ProblemProperties
@@ -69 +69 @@
         // Parameter Names
 
+        private const string PopulationSizeName = "Population Size";
+        private const string MaximumGenerationsName = "Maximum Generations";
+        private const string CrossoverProbabilityName = "Crossover Probability";
+        private const string MutationProbabilityName = "Mutation Probability";
+        private const string DominateOnEqualQualitiesName = "Dominate On Equal Qualities";
+        private const string SetSeedRandomlyName = "Set Seed Randomly";
         private const string SeedName = "Seed";
-        private const string SetSeedRandomlyName = "Set Seed Randomly";
-        private const string PopulationSizeName = "Population Size";
-        private const string CrossoverProbabilityName = "Crossover Probability";
-        private const string CrossoverContiguityName = "Crossover Contiguity";
-        private const string MutationProbabilityName = "Mutation Probability";
-        private const string MaximumGenerationsName = "Maximum Generations";
-        private const string DominateOnEqualQualitiesName = "Dominate On Equal Qualities";
 
         // Results Names
@@ -91 +90 @@
         #region ParameterProperties
 
+        private IFixedValueParameter<IntValue> PopulationSizeParameter
+        {
+            get { return (IFixedValueParameter<IntValue>)Parameters[PopulationSizeName]; }
+        }
+
+        private IFixedValueParameter<IntValue> MaximumGenerationsParameter
+        {
+            get { return (IFixedValueParameter<IntValue>)Parameters[MaximumGenerationsName]; }
+        }
+
+        private IFixedValueParameter<PercentValue> CrossoverProbabilityParameter
+        {
+            get { return (IFixedValueParameter<PercentValue>)Parameters[CrossoverProbabilityName]; }
+        }
+
+        private IFixedValueParameter<PercentValue> MutationProbabilityParameter
+        {
+            get { return (IFixedValueParameter<PercentValue>)Parameters[MutationProbabilityName]; }
+        }
+
+        private IFixedValueParameter<BoolValue> DominateOnEqualQualitiesParameter
+        {
+            get { return (IFixedValueParameter<BoolValue>)Parameters[DominateOnEqualQualitiesName]; }
+        }
+
+        private IFixedValueParameter<BoolValue> SetSeedRandomlyParameter
+        {
+            get { return (IFixedValueParameter<BoolValue>)Parameters[SetSeedRandomlyName]; }
+        }
+
         private IFixedValueParameter<IntValue> SeedParameter
         {
@@ -96 +125 @@
         }
 
-        private IFixedValueParameter<BoolValue> SetSeedRandomlyParameter
-        {
-            get { return (IFixedValueParameter<BoolValue>)Parameters[SetSeedRandomlyName]; }
-        }
-
-        private IFixedValueParameter<IntValue> PopulationSizeParameter
-        {
-            get { return (IFixedValueParameter<IntValue>)Parameters[PopulationSizeName]; }
-        }
-
-        private IFixedValueParameter<PercentValue> CrossoverProbabilityParameter
-        {
-            get { return (IFixedValueParameter<PercentValue>)Parameters[CrossoverProbabilityName]; }
-        }
-
-        private IFixedValueParameter<DoubleValue> CrossoverContiguityParameter
-        {
-            get { return (IFixedValueParameter<DoubleValue>)Parameters[CrossoverContiguityName]; }
-        }
-
-        private IFixedValueParameter<PercentValue> MutationProbabilityParameter
-        {
-            get { return (IFixedValueParameter<PercentValue>)Parameters[MutationProbabilityName]; }
-        }
-
-        private IFixedValueParameter<IntValue> MaximumGenerationsParameter
-        {
-            get { return (IFixedValueParameter<IntValue>)Parameters[MaximumGenerationsName]; }
-        }
-
-        private IFixedValueParameter<BoolValue> DominateOnEqualQualitiesParameter
-        {
-            get { return (IFixedValueParameter<BoolValue>)Parameters[DominateOnEqualQualitiesName]; }
-        }
-
         #endregion ParameterProperties
 
         #region Properties
 
+        public IntValue PopulationSize => PopulationSizeParameter.Value;
+
+        public IntValue MaximumGenerations => MaximumGenerationsParameter.Value;
+
+        public PercentValue CrossoverProbability => CrossoverProbabilityParameter.Value;
+
+        public PercentValue MutationProbability => MutationProbabilityParameter.Value;
+
+        public BoolValue DominateOnEqualQualities => DominateOnEqualQualitiesParameter.Value;
+
+        public BoolValue SetSeedRandomly => SetSeedRandomlyParameter.Value;
         public IntValue Seed => SeedParameter.Value;
-
-        public BoolValue SetSeedRandomly => SetSeedRandomlyParameter.Value;
-
-        public IntValue PopulationSize => PopulationSizeParameter.Value;
-
-        public PercentValue CrossoverProbability => CrossoverProbabilityParameter.Value;
-
-        public DoubleValue CrossoverContiguity => CrossoverContiguityParameter.Value;
-
-        public PercentValue MutationProbability => MutationProbabilityParameter.Value;
-
-        public IntValue MaximumGenerations => MaximumGenerationsParameter.Value;
-
-        public BoolValue DominateOnEqualQualities => DominateOnEqualQualitiesParameter.Value;
 
         // todo: create one property for the Generated Reference Points and one for the current
@@ -164 +155 @@
         }
 
+        public IntValue ResultsCurrentGeneration
+        {
+            get { return (IntValue)Results[CurrentGenerationResultName].Value; }
+            set { Results[CurrentGenerationResultName].Value = value; }
+        }
+
+        public DoubleValue ResultsGenerationalDistance
+        {
+            get { return (DoubleValue)Results[GenerationalDistanceResultName].Value; }
+            set { Results[GenerationalDistanceResultName].Value = value; }
+        }
+
+        public DoubleValue ResultsInvertedGenerationalDistance
+        {
+            get { return (DoubleValue)Results[InvertedGenerationalDistanceResultName].Value; }
+            set { Results[InvertedGenerationalDistanceResultName].Value = value; }
+        }
+
+        public ParetoFrontScatterPlot ResultsScatterPlot
+        {
+            get { return (ParetoFrontScatterPlot)Results[ScatterPlotResultName].Value; }
+            set { Results[ScatterPlotResultName].Value = value; }
+        }
+
         public DoubleMatrix ResultsSolutions
         {
@@ -170 +185 @@
         }
 
-        public IntValue ResultsCurrentGeneration
-        {
-            get { return (IntValue)Results[CurrentGenerationResultName].Value; }
-            set { Results[CurrentGenerationResultName].Value = value; }
-        }
-
-        public DoubleValue ResultsGenerationalDistance
-        {
-            get { return (DoubleValue)Results[GenerationalDistanceResultName].Value; }
-            set { Results[GenerationalDistanceResultName].Value = value; }
-        }
-
-        public DoubleValue ResultsInvertedGenerationalDistance
-        {
-            get { return (DoubleValue)Results[InvertedGenerationalDistanceResultName].Value; }
-            set { Results[InvertedGenerationalDistanceResultName].Value = value; }
-        }
-
-        public ParetoFrontScatterPlot ResultsScatterPlot
-        {
-            get { return (ParetoFrontScatterPlot)Results[ScatterPlotResultName].Value; }
-            set { Results[ScatterPlotResultName].Value = value; }
-        }
-
         #endregion ResultsProperties
 
@@ -200 +191 @@
         public NSGA3() : base()
         {
+            Parameters.Add(new FixedValueParameter<IntValue>(PopulationSizeName, "The size of the population of Individuals.", new IntValue(200)));
+            Parameters.Add(new FixedValueParameter<IntValue>(MaximumGenerationsName, "The maximum number of generations which should be processed.", new IntValue(1000)));
+            Parameters.Add(new FixedValueParameter<PercentValue>(CrossoverProbabilityName, "The probability that the crossover operator is applied on two parents.", new PercentValue(1.0)));
+            Parameters.Add(new FixedValueParameter<PercentValue>(MutationProbabilityName, "The probability that the mutation operator is applied on a Individual.", new PercentValue(0.05)));
+            Parameters.Add(new FixedValueParameter<BoolValue>(DominateOnEqualQualitiesName, "Flag which determines wether Individuals with equal quality values should be treated as dominated.", new BoolValue(false)));
+            Parameters.Add(new FixedValueParameter<BoolValue>(SetSeedRandomlyName, "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
             Parameters.Add(new FixedValueParameter<IntValue>(SeedName, "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
-            Parameters.Add(new FixedValueParameter<BoolValue>(SetSeedRandomlyName, "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
-            Parameters.Add(new FixedValueParameter<IntValue>(PopulationSizeName, "The size of the population of Individuals.", new IntValue(200)));
-            Parameters.Add(new FixedValueParameter<PercentValue>(CrossoverProbabilityName, "The probability that the crossover operator is applied on two parents.", new PercentValue(1.0)));
-            Parameters.Add(new FixedValueParameter<DoubleValue>(CrossoverContiguityName, "The contiguity value for the Simulated Binary Crossover that specifies how close a child should be to its parents (larger value means closer). The value must be greater than or equal than 0. Typical values are in the range [2;5]."));
-            Parameters.Add(new FixedValueParameter<PercentValue>(MutationProbabilityName, "The probability that the mutation operator is applied on a Individual.", new PercentValue(0.05)));
-            Parameters.Add(new FixedValueParameter<IntValue>(MaximumGenerationsName, "The maximum number of generations which should be processed.", new IntValue(1000)));
-            Parameters.Add(new FixedValueParameter<BoolValue>(DominateOnEqualQualitiesName, "Flag which determines wether Individuals with equal quality values should be treated as dominated.", new BoolValue(false)));
         }
 
@@ -222 +212 @@
             // todo: don't forget to clone storable fields
             random = cloner.Clone(original.random);
-            solutions = original.solutions != null ? original.solutions.Select(cloner.Clone).ToList() : null;
-            referencePoints = original.referencePoints != null ? original.referencePoints.Select(r =>
+            solutions = original.solutions?.Select(cloner.Clone).ToList();
+            referencePoints = original.referencePoints?.Select(r =>
             {
                 var refPoint = new ReferencePoint(random, r.NumberOfDimensions);
                 r.Values.CopyTo(refPoint.Values, 0);
                 return refPoint;
-            }).ToList() : null;
+            }).ToList();
         }
 
@@ -244 +234 @@
             base.Initialize(cancellationToken);
 
-            // Set population size
-            int numberOfGeneratedReferencePoints = ReferencePoint.GetNumberOfGeneratedReferencePoints(NumberOfObjectives);
-            int pop = ((numberOfGeneratedReferencePoints + 3) / 4) * 4;
-            PopulationSize.Value = pop;
-
-            // Set mutation probability
-            double mutationProbability = 1.0 / Problem.Encoding.Length;
-            MutationProbability.Value = mutationProbability;
-
+            SetParameters();
             InitResults();
             InitFields();
@@ -258 +240 @@
         }
 
+        private void SetParameters()
+        {
+            // Set population size
+            int numberOfGeneratedReferencePoints = ReferencePoint.GetNumberOfGeneratedReferencePoints(NumberOfObjectives);
+            PopulationSize.Value = ReferencePoint.GetPopulationSizeForReferencePoints(numberOfGeneratedReferencePoints);
+
+            // Set mutation probability
+            MutationProbability.Value = 1.0 / Problem.Encoding.Length;
+        }
+
         private void InitResults()
         {
             Results.Add(new Result(GeneratedReferencePointsResultName, "The initially generated reference points", new DoubleMatrix()));
-            Results.Add(new Result(CurrentFrontResultName, "The Pareto Front", new DoubleMatrix()));
             Results.Add(new Result(CurrentGenerationResultName, "The current generation", new IntValue(1)));
             Results.Add(new Result(GenerationalDistanceResultName, "The generational distance to an optimal pareto front defined in the Problem", new DoubleValue(double.NaN)));
             Results.Add(new Result(InvertedGenerationalDistanceResultName, "The inverted generational distance to an optimal pareto front defined in the Problem", new DoubleValue(double.NaN)));
             Results.Add(new Result(ScatterPlotResultName, "A scatterplot displaying the evaluated solutions and (if available) the analytically optimal front", new ParetoFrontScatterPlot()));
-
-            var problem = Problem as MultiObjectiveTestFunctionProblem;
-            if (problem == null) return;
+            Results.Add(new Result(CurrentFrontResultName, "The Pareto Front", new DoubleMatrix()));
+
+            if (!(Problem is MultiObjectiveTestFunctionProblem problem)) return;
             // todo: add BestKnownFront parameter
             ResultsScatterPlot = new ParetoFrontScatterPlot(new double[0][], new double[0][], problem.BestKnownFront.ToJaggedArray(), problem.Objectives, problem.ProblemSize);
@@ -276 +267 @@
         {
             random = new MersenneTwister();
-            InitSolutions();
+            solutions = GetInitialPopulation();
             InitReferencePoints();
         }
@@ -287 +278 @@
         }
 
-        private void InitSolutions()
-        {
-            int minBound = 0;
-            int maxBound = 1;
+        private List<Solution> GetInitialPopulation()
+        {
+            var problem = Problem as MultiObjectiveTestFunctionProblem;
+            if (problem.Bounds.Rows != 1) throw new Exception();
 
             // Initialise solutions
-            solutions = new List<Solution>(PopulationSize.Value);
+            List<Solution> solutions = new List<Solution>(PopulationSize.Value);
             for (int i = 0; i < PopulationSize.Value; i++)
             {
+                double minBound = problem.Bounds[0, 0];
+                double maxBound = problem.Bounds[0, 1];
                 RealVector randomRealVector = new RealVector(Problem.Encoding.Length, random, minBound, maxBound);
                 var solution = new Solution(randomRealVector);
@@ -301 +294 @@
                 solutions.Add(solution);
             }
+
+            return solutions;
         }
 
@@ -361 +356 @@
             ResultsSolutions = solutions.Select(s => s.Chromosome.ToArray()).ToMatrix();
 
-            var problem = Problem as MultiObjectiveTestFunctionProblem;
-            if (problem == null) return;
+            if (!(Problem is MultiObjectiveTestFunctionProblem problem)) return;
 
             ResultsGenerationalDistance = new DoubleValue(problem.BestKnownFront != null ? GenerationalDistance.Calculate(solutions.Select(s => s.Fitness), problem.BestKnownFront.ToJaggedArray(), 1) : double.NaN);
@@ -433 +427 @@
 
                     double rnd = random.NextDouble();
-                    var deltaq = 0.0;
+                    double deltaq;
                     if (rnd <= 0.5)
                     {
@@ -447 +441 @@
                     }
 
-                    y = y + deltaq * (ub - lb);
+                    y += deltaq * (ub - lb);
                     y = Math.Min(ub, Math.Max(lb, y));
 

branches/2825-NSGA3/HeuristicLab.Algorithms.NSGA3/3.3/ReferencePoint.cs

@@ -4 +4 @@
 using HeuristicLab.Common;
 using HeuristicLab.Core;
-using HeuristicLab.Optimization;
 
 namespace HeuristicLab.Algorithms.NSGA3
@@ -10 +9 @@
     public class ReferencePoint : IDeepCloneable
     {
-
         #region Properties
 
@@ -109 +107 @@
             int innerPoints = innerDiv == 0 ? 0 : Utility.NCR(nDim + innerDiv - 1, innerDiv);
             return outerPoints + innerPoints;
+        }
+
+        public static int GetPopulationSizeForReferencePoints(int referencePoints)
+        {
+            return (referencePoints + 3) / 4 * 4;
         }