private TerminationOperator(TerminationOperator original, Cloner cloner) : base(original, cloner) { }
public RandomSearchAlgorithm() : base() { #region Add parameters Parameters.Add(new FixedValueParameter<IntValue>("Seed", "The random seed used to initialize the new pseudo random number generator.", new IntValue(0))); Parameters.Add(new FixedValueParameter<BoolValue>("SetSeedRandomly", "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true))); Parameters.Add(new FixedValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze the solutions each iteration.", new MultiAnalyzer())); Parameters.Add(new FixedValueParameter<IntValue>("MaximumEvaluatedSolutions", "The number of random solutions the algorithm should evaluate.", new IntValue(1000))); Parameters.Add(new FixedValueParameter<IntValue>("BatchSize", "The number of random solutions that are evaluated (in parallel) per iteration.", new IntValue(100))); Parameters.Add(new FixedValueParameter<IntValue>("MaximumIterations", "The number of iterations that the algorithm will run.", new IntValue(10)) { Hidden = true }); Parameters.Add(new FixedValueParameter<MultiTerminator>("Terminator", "The termination criteria that defines if the algorithm should continue or stop.", new MultiTerminator()) { Hidden = true }); #endregion #region Create operators var randomCreator = new RandomCreator(); var variableCreator = new VariableCreator() { Name = "Initialize Variables" }; var resultsCollector = new ResultsCollector(); var solutionCreator = new SolutionsCreator() { Name = "Create Solutions" }; var analyzerPlaceholder = new Placeholder() { Name = "Analyzer (Placeholder)" }; var evaluationsCounter = new IntCounter() { Name = "Increment EvaluatedSolutions" }; var subScopesRemover = new SubScopesRemover(); var iterationsCounter = new IntCounter() { Name = "Increment Iterations" }; var terminationOperator = new TerminationOperator(); #endregion #region Create and parameterize operator graph OperatorGraph.InitialOperator = randomCreator; randomCreator.SeedParameter.Value = null; randomCreator.SeedParameter.ActualName = SeedParameter.Name; randomCreator.SetSeedRandomlyParameter.Value = null; randomCreator.SetSeedRandomlyParameter.ActualName = SetSeedRandomlyParameter.Name; randomCreator.Successor = variableCreator; variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("Iterations", new IntValue(0))); variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("EvaluatedSolutions", new IntValue(0))); variableCreator.Successor = resultsCollector; resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("Iterations", "The current iteration number.")); resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("EvaluatedSolutions", "The current number of evaluated solutions.")); resultsCollector.Successor = solutionCreator; solutionCreator.NumberOfSolutionsParameter.ActualName = BatchSizeParameter.Name; solutionCreator.ParallelParameter.Value.Value = true; solutionCreator.Successor = evaluationsCounter; evaluationsCounter.ValueParameter.ActualName = "EvaluatedSolutions"; evaluationsCounter.Increment = null; evaluationsCounter.IncrementParameter.ActualName = BatchSizeParameter.Name; evaluationsCounter.Successor = analyzerPlaceholder; analyzerPlaceholder.OperatorParameter.ActualName = AnalyzerParameter.Name; analyzerPlaceholder.Successor = subScopesRemover; subScopesRemover.RemoveAllSubScopes = true; subScopesRemover.Successor = iterationsCounter; iterationsCounter.ValueParameter.ActualName = "Iterations"; iterationsCounter.Increment = new IntValue(1); iterationsCounter.Successor = terminationOperator; terminationOperator.TerminatorParameter.ActualName = TerminatorParameter.Name; terminationOperator.ContinueBranch = solutionCreator; #endregion #region Create analyzers singleObjectiveQualityAnalyzer = new BestAverageWorstQualityAnalyzer(); #endregion #region Create terminators evaluationsTerminator = new ComparisonTerminator<IntValue>("EvaluatedSolutions", ComparisonType.Less, MaximumEvaluatedSolutionsParameter) { Name = "Evaluated solutions." }; qualityTerminator = new SingleObjectiveQualityTerminator() { Name = "Quality" }; executionTimeTerminator = new ExecutionTimeTerminator(this, new TimeSpanValue(TimeSpan.FromMinutes(5))); #endregion #region Parameterize UpdateAnalyzers(); ParameterizeAnalyzers(); UpdateTerminators(); #endregion Initialize(); }
public AlpsOffspringSelectionGeneticAlgorithmMainLoop() : base() { Parameters.Add(new ValueLookupParameter<IRandom>("GlobalRandom", "A pseudo random number generator.")); Parameters.Add(new ValueLookupParameter<IRandom>("LocalRandom", "A pseudo random number generator.")); Parameters.Add(new ValueLookupParameter<IOperator>("Evaluator", "The operator used to evaluate solutions. This operator is executed in parallel, if an engine is used which supports parallelization.")); Parameters.Add(new ValueLookupParameter<IntValue>("EvaluatedSolutions", "The number of times solutions have been evaluated.")); Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality", "The value which represents the quality of a solution.")); Parameters.Add(new ValueLookupParameter<BoolValue>("Maximization", "True if the problem is a maximization problem, otherwise false.")); Parameters.Add(new ValueLookupParameter<IOperator>("Analyzer", "The operator used to analyze all individuals from all layers combined.")); Parameters.Add(new ValueLookupParameter<IOperator>("LayerAnalyzer", "The operator used to analyze each layer.")); Parameters.Add(new ValueLookupParameter<IntValue>("NumberOfLayers", "The number of layers.")); Parameters.Add(new ValueLookupParameter<IntValue>("PopulationSize", "The size of the population of solutions in each layer.")); Parameters.Add(new LookupParameter<IntValue>("CurrentPopulationSize", "The current size of the population.")); Parameters.Add(new ValueLookupParameter<IOperator>("Selector", "The operator used to select solutions for reproduction.")); Parameters.Add(new ValueLookupParameter<IOperator>("Crossover", "The operator used to cross solutions.")); Parameters.Add(new ValueLookupParameter<IOperator>("Mutator", "The operator used to mutate solutions.")); Parameters.Add(new ValueLookupParameter<PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution.")); Parameters.Add(new ValueLookupParameter<IntValue>("Elites", "The numer of elite solutions which are kept in each generation.")); Parameters.Add(new ValueLookupParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)")); Parameters.Add(new ValueLookupParameter<DoubleValue>("SuccessRatio", "The ratio of successful to total children that should be achieved.")); Parameters.Add(new ValueLookupParameter<DoubleValue>("ComparisonFactor", "The comparison factor is used to determine whether the offspring should be compared to the better parent, the worse parent or a quality value linearly interpolated between them. It is in the range [0;1].")); Parameters.Add(new ValueLookupParameter<DoubleValue>("MaximumSelectionPressure", "The maximum selection pressure that terminates the algorithm.")); Parameters.Add(new ValueLookupParameter<BoolValue>("OffspringSelectionBeforeMutation", "True if the offspring selection step should be applied before mutation, false if it should be applied after mutation.")); Parameters.Add(new ValueLookupParameter<BoolValue>("FillPopulationWithParents", "True if the population should be filled with parent individual or false if worse children should be used when the maximum selection pressure is exceeded.")); Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Age", "The age of individuals.")); Parameters.Add(new ValueLookupParameter<IntValue>("AgeGap", "The frequency of reseeding the lowest layer and scaling factor for the age-limits for the layers.")); Parameters.Add(new ValueLookupParameter<DoubleValue>("AgeInheritance", "A weight that determines the age of a child after crossover based on the older (1.0) and younger (0.0) parent.")); Parameters.Add(new ValueLookupParameter<IntArray>("AgeLimits", "The maximum age an individual is allowed to reach in a certain layer.")); Parameters.Add(new ValueLookupParameter<IntValue>("MatingPoolRange", "The range of sub - populations used for creating a mating pool. (1 = current + previous sub-population)")); Parameters.Add(new ValueLookupParameter<BoolValue>("ReduceToPopulationSize", "Reduce the CurrentPopulationSize after elder migration to PopulationSize")); Parameters.Add(new ValueLookupParameter<IOperator>("Terminator", "The termination criteria that defines if the algorithm should continue or stop")); var variableCreator = new VariableCreator() { Name = "Initialize" }; var initLayerAnalyzerProcessor = new SubScopesProcessor(); var layerVariableCreator = new VariableCreator() { Name = "Initialize Layer" }; var initLayerAnalyzerPlaceholder = new Placeholder() { Name = "LayerAnalyzer (Placeholder)" }; var layerResultCollector = new ResultsCollector() { Name = "Collect layer results" }; var initAnalyzerPlaceholder = new Placeholder() { Name = "Analyzer (Placeholder)" }; var resultsCollector = new ResultsCollector(); var matingPoolCreator = new MatingPoolCreator() { Name = "Create Mating Pools" }; var matingPoolProcessor = new UniformSubScopesProcessor() { Name = "Process Mating Pools" }; var initializeLayer = new Assigner() { Name = "Reset LayerEvaluatedSolutions" }; var mainOperator = new AlpsOffspringSelectionGeneticAlgorithmMainOperator(); var generationsIcrementor = new IntCounter() { Name = "Increment Generations" }; var evaluatedSolutionsReducer = new DataReducer() { Name = "Increment EvaluatedSolutions" }; var eldersEmigrator = CreateEldersEmigrator(); var layerOpener = CreateLayerOpener(); var layerReseeder = CreateReseeder(); var layerAnalyzerProcessor = new UniformSubScopesProcessor(); var layerAnalyzerPlaceholder = new Placeholder() { Name = "LayerAnalyzer (Placeholder)" }; var analyzerPlaceholder = new Placeholder() { Name = "Analyzer (Placeholder)" }; var termination = new TerminationOperator(); OperatorGraph.InitialOperator = variableCreator; variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("Generations", new IntValue(0))); variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("OpenLayers", new IntValue(1))); variableCreator.Successor = initLayerAnalyzerProcessor; initLayerAnalyzerProcessor.Operators.Add(layerVariableCreator); initLayerAnalyzerProcessor.Successor = initAnalyzerPlaceholder; layerVariableCreator.CollectedValues.Add(new ValueParameter<IntValue>("Layer", new IntValue(0))); layerVariableCreator.CollectedValues.Add(new ValueParameter<ResultCollection>("LayerResults")); layerVariableCreator.CollectedValues.Add(new ValueParameter<DoubleValue>("SelectionPressure", new DoubleValue(0))); layerVariableCreator.CollectedValues.Add(new ValueParameter<DoubleValue>("CurrentSuccessRatio", new DoubleValue(0))); layerVariableCreator.Successor = initLayerAnalyzerPlaceholder; initLayerAnalyzerPlaceholder.OperatorParameter.ActualName = LayerAnalyzerParameter.Name; initLayerAnalyzerPlaceholder.Successor = layerResultCollector; layerResultCollector.ResultsParameter.ActualName = "LayerResults"; layerResultCollector.CollectedValues.Add(new LookupParameter<DoubleValue>("Current Selection Pressure", "Displays the rising selection pressure during a generation.", "SelectionPressure")); layerResultCollector.CollectedValues.Add(new LookupParameter<DoubleValue>("Current Success Ratio", "Indicates how many successful children were already found during a generation (relative to the population size).", "CurrentSuccessRatio")); layerResultCollector.Successor = null; initAnalyzerPlaceholder.OperatorParameter.ActualName = AnalyzerParameter.Name; initAnalyzerPlaceholder.Successor = resultsCollector; resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("Generations")); resultsCollector.CollectedValues.Add(new ScopeTreeLookupParameter<ResultCollection>("LayerResults", "Result set for each Layer", "LayerResults")); resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("OpenLayers")); resultsCollector.CopyValue = new BoolValue(false); resultsCollector.Successor = matingPoolCreator; matingPoolCreator.MatingPoolRangeParameter.Value = null; matingPoolCreator.MatingPoolRangeParameter.ActualName = MatingPoolRangeParameter.Name; matingPoolCreator.Successor = matingPoolProcessor; matingPoolProcessor.Parallel.Value = true; matingPoolProcessor.Operator = initializeLayer; matingPoolProcessor.Successor = generationsIcrementor; initializeLayer.LeftSideParameter.ActualName = "LayerEvaluatedSolutions"; initializeLayer.RightSideParameter.Value = new IntValue(0); initializeLayer.Successor = mainOperator; mainOperator.RandomParameter.ActualName = LocalRandomParameter.Name; mainOperator.EvaluatorParameter.ActualName = EvaluatorParameter.Name; mainOperator.EvaluatedSolutionsParameter.ActualName = "LayerEvaluatedSolutions"; mainOperator.QualityParameter.ActualName = QualityParameter.Name; mainOperator.MaximizationParameter.ActualName = MaximizationParameter.Name; mainOperator.PopulationSizeParameter.ActualName = PopulationSizeParameter.Name; mainOperator.SelectorParameter.ActualName = SelectorParameter.Name; mainOperator.CrossoverParameter.ActualName = CrossoverParameter.Name; mainOperator.MutatorParameter.ActualName = MutatorParameter.ActualName; mainOperator.MutationProbabilityParameter.ActualName = MutationProbabilityParameter.Name; mainOperator.ElitesParameter.ActualName = ElitesParameter.Name; mainOperator.ReevaluateElitesParameter.ActualName = ReevaluateElitesParameter.Name; mainOperator.ComparisonFactorParameter.ActualName = ComparisonFactorParameter.Name; mainOperator.SuccessRatioParameter.ActualName = SuccessRatioParameter.Name; mainOperator.CurrentSuccessRatioParameter.ActualName = "CurrentSuccessRatio"; mainOperator.SelectionPressureParameter.ActualName = "SelectionPressure"; mainOperator.MaximumSelectionPressureParameter.ActualName = MaximumSelectionPressureParameter.Name; mainOperator.OffspringSelectionBeforeMutationParameter.ActualName = OffspringSelectionBeforeMutationParameter.Name; mainOperator.FillPopulationWithParentsParameter.ActualName = FillPopulationWithParentsParameter.Name; mainOperator.AgeParameter.ActualName = AgeParameter.Name; mainOperator.AgeInheritanceParameter.ActualName = AgeInheritanceParameter.Name; mainOperator.AgeIncrementParameter.Value = new DoubleValue(1.0); mainOperator.Successor = null; generationsIcrementor.ValueParameter.ActualName = "Generations"; generationsIcrementor.Increment = new IntValue(1); generationsIcrementor.Successor = evaluatedSolutionsReducer; evaluatedSolutionsReducer.ParameterToReduce.ActualName = "LayerEvaluatedSolutions"; evaluatedSolutionsReducer.TargetParameter.ActualName = EvaluatedSolutionsParameter.Name; evaluatedSolutionsReducer.ReductionOperation.Value = new ReductionOperation(ReductionOperations.Sum); evaluatedSolutionsReducer.TargetOperation.Value = new ReductionOperation(ReductionOperations.Sum); evaluatedSolutionsReducer.Successor = eldersEmigrator; eldersEmigrator.Successor = layerOpener; layerOpener.Successor = layerReseeder; layerReseeder.Successor = layerAnalyzerProcessor; layerAnalyzerProcessor.Operator = layerAnalyzerPlaceholder; layerAnalyzerProcessor.Successor = analyzerPlaceholder; layerAnalyzerPlaceholder.OperatorParameter.ActualName = LayerAnalyzerParameter.Name; analyzerPlaceholder.OperatorParameter.ActualName = AnalyzerParameter.Name; analyzerPlaceholder.Successor = termination; termination.TerminatorParameter.ActualName = TerminatorParameter.Name; termination.ContinueBranch = matingPoolCreator; }