private SubScopesSorter(SubScopesSorter original, Cloner cloner)
: base(original, cloner)
{
}
public CMAEvolutionStrategy()
: base() {
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, "λ (lambda) - the size of the offspring population.", new IntValue(20)));
Parameters.Add(new FixedValueParameter<IntValue>(InitialIterationsName, "The number of iterations that should be performed with only axis parallel mutation.", new IntValue(0)));
Parameters.Add(new FixedValueParameter<DoubleArray>(InitialSigmaName, "The initial sigma can be a single value or a value for each dimension. All values need to be > 0.", new DoubleArray(new[] { 0.5 })));
Parameters.Add(new OptionalValueParameter<IntValue>(MuName, "Optional, the mu best offspring that should be considered for update of the new mean and strategy parameters. If not given it will be automatically calculated."));
Parameters.Add(new ConstrainedValueParameter<ICMARecombinator>(CMARecombinatorName, "The operator used to calculate the new mean."));
Parameters.Add(new ConstrainedValueParameter<ICMAManipulator>(CMAMutatorName, "The operator used to manipulate a point."));
Parameters.Add(new ConstrainedValueParameter<ICMAInitializer>(CMAInitializerName, "The operator that initializes the covariance matrix and strategy parameters."));
Parameters.Add(new ConstrainedValueParameter<ICMAUpdater>(CMAUpdaterName, "The operator that updates the covariance matrix and strategy parameters."));
Parameters.Add(new ValueParameter<MultiAnalyzer>(AnalyzerName, "The operator used to analyze each generation.", new MultiAnalyzer()));
Parameters.Add(new FixedValueParameter<IntValue>(MaximumGenerationsName, "The maximum number of generations which should be processed.", new IntValue(1000)));
Parameters.Add(new FixedValueParameter<IntValue>(MaximumEvaluatedSolutionsName, "The maximum number of evaluated solutions that should be computed.", new IntValue(int.MaxValue)));
Parameters.Add(new FixedValueParameter<DoubleValue>(TargetQualityName, "(stopFitness) Surpassing this quality value terminates the algorithm.", new DoubleValue(double.NaN)));
Parameters.Add(new FixedValueParameter<DoubleValue>(MinimumQualityChangeName, "(stopTolFun) If the range of fitness values is less than a certain value the algorithm terminates (set to 0 or positive value to enable).", new DoubleValue(double.NaN)));
Parameters.Add(new FixedValueParameter<DoubleValue>(MinimumQualityHistoryChangeName, "(stopTolFunHist) If the range of fitness values is less than a certain value for a certain time the algorithm terminates (set to 0 or positive to enable).", new DoubleValue(double.NaN)));
Parameters.Add(new FixedValueParameter<DoubleValue>(MinimumStandardDeviationName, "(stopTolXFactor) If the standard deviation falls below a certain value the algorithm terminates (set to 0 or positive to enable).", new DoubleValue(double.NaN)));
Parameters.Add(new FixedValueParameter<DoubleValue>(MaximumStandardDeviationChangeName, "(stopTolUpXFactor) If the standard deviation changes by a value larger than this parameter the algorithm stops (set to a value > 0 to enable).", new DoubleValue(double.NaN)));
var randomCreator = new RandomCreator();
var variableCreator = new VariableCreator();
var resultsCollector = new ResultsCollector();
var cmaInitializer = new Placeholder();
solutionCreator = new Placeholder();
var subScopesCreator = new SubScopesCreator();
var ussp1 = new UniformSubScopesProcessor();
populationSolutionCreator = new Placeholder();
var cmaMutator = new Placeholder();
var ussp2 = new UniformSubScopesProcessor();
evaluator = new Placeholder();
var subScopesCounter = new SubScopesCounter();
sorter = new SubScopesSorter();
var analyzer = new Placeholder();
var cmaRecombinator = new Placeholder();
var generationsCounter = new IntCounter();
var cmaUpdater = new Placeholder();
terminator = new Terminator();
OperatorGraph.InitialOperator = randomCreator;
randomCreator.RandomParameter.ActualName = "Random";
randomCreator.SeedParameter.ActualName = SeedParameter.Name;
randomCreator.SeedParameter.Value = null;
randomCreator.SetSeedRandomlyParameter.ActualName = SetSeedRandomlyParameter.Name;
randomCreator.SetSeedRandomlyParameter.Value = null;
randomCreator.Successor = variableCreator;
variableCreator.Name = "Initialize Variables";
variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("EvaluatedSolutions", new IntValue(0)));
variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("Generations", new IntValue(0)));
variableCreator.Successor = resultsCollector;
resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("EvaluatedSolutions"));
resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("Generations"));
resultsCollector.ResultsParameter.ActualName = "Results";
resultsCollector.Successor = cmaInitializer;
cmaInitializer.Name = "Initialize Strategy Parameters";
cmaInitializer.OperatorParameter.ActualName = CMAInitializerParameter.Name;
cmaInitializer.Successor = subScopesCreator;
subScopesCreator.NumberOfSubScopesParameter.ActualName = PopulationSizeParameter.Name;
subScopesCreator.Successor = ussp1;
ussp1.Name = "Create population";
ussp1.Parallel = new BoolValue(false);
ussp1.Operator = populationSolutionCreator;
ussp1.Successor = solutionCreator;
populationSolutionCreator.Name = "Initialize arx";
// populationSolutionCreator.OperatorParameter will be wired
populationSolutionCreator.Successor = null;
solutionCreator.Name = "Initialize xmean";
// solutionCreator.OperatorParameter will be wired
solutionCreator.Successor = cmaMutator;
cmaMutator.Name = "Sample population";
cmaMutator.OperatorParameter.ActualName = CMAMutatorParameter.Name;
cmaMutator.Successor = ussp2;
ussp2.Name = "Evaluate offspring";
ussp2.Parallel = new BoolValue(true);
ussp2.Operator = evaluator;
ussp2.Successor = subScopesCounter;
evaluator.Name = "Evaluator";
// evaluator.OperatorParameter will be wired
evaluator.Successor = null;
subScopesCounter.Name = "Count EvaluatedSolutions";
subScopesCounter.AccumulateParameter.Value = new BoolValue(true);
subScopesCounter.ValueParameter.ActualName = "EvaluatedSolutions";
subScopesCounter.Successor = sorter;
// sorter.ValueParameter will be wired
// sorter.DescendingParameter will be wired
sorter.Successor = analyzer;
analyzer.Name = "Analyzer";
analyzer.OperatorParameter.ActualName = AnalyzerParameter.Name;
analyzer.Successor = cmaRecombinator;
cmaRecombinator.Name = "Create new xmean";
cmaRecombinator.OperatorParameter.ActualName = CMARecombinatorParameter.Name;
cmaRecombinator.Successor = generationsCounter;
generationsCounter.Name = "Generations++";
generationsCounter.IncrementParameter.Value = new IntValue(1);
generationsCounter.ValueParameter.ActualName = "Generations";
generationsCounter.Successor = cmaUpdater;
cmaUpdater.Name = "Update distributions";
cmaUpdater.OperatorParameter.ActualName = CMAUpdaterParameter.Name;
cmaUpdater.Successor = terminator;
terminator.Continue = cmaMutator;
terminator.Terminate = null;
qualityAnalyzer = new BestAverageWorstQualityAnalyzer();
cmaAnalyzer = new CMAAnalyzer();
InitializeOperators();
RegisterEventHandlers();
Parameterize();
}
private void Initialize() {
#region Create parameters
Parameters.Add(new ValueLookupParameter<IRandom>("Random", "A pseudo random number generator."));
Parameters.Add(new ValueLookupParameter<BoolValue>("Maximization", "True if the problem is a maximization problem, otherwise false."));
Parameters.Add(new LookupParameter<DoubleValue>("Quality", "The value which represents the quality of a solution."));
Parameters.Add(new ValueLookupParameter<DoubleValue>("BestKnownQuality", "The best known quality value found so far."));
Parameters.Add(new LookupParameter<DoubleValue>("MoveQuality", "The value which represents the quality of a move."));
Parameters.Add(new LookupParameter<BoolValue>("MoveTabu", "The value that indicates if a move is tabu or not."));
Parameters.Add(new ValueLookupParameter<IntValue>("MaximumIterations", "The maximum number of generations which should be processed."));
Parameters.Add(new ValueLookupParameter<IntValue>("TabuTenure", "The length of the tabu list, and also means the number of iterations a move is kept tabu"));
Parameters.Add(new ValueLookupParameter<IOperator>("MoveGenerator", "The operator that generates the moves."));
Parameters.Add(new ValueLookupParameter<IOperator>("MoveMaker", "The operator that performs a move and updates the quality."));
Parameters.Add(new ValueLookupParameter<IOperator>("MoveEvaluator", "The operator that evaluates a move."));
Parameters.Add(new ValueLookupParameter<IOperator>("TabuChecker", "The operator that checks whether a move is tabu."));
Parameters.Add(new ValueLookupParameter<IOperator>("TabuMaker", "The operator that declares a move tabu."));
Parameters.Add(new ValueLookupParameter<IOperator>("Analyzer", "The operator used to analyze the solution and moves."));
Parameters.Add(new ValueLookupParameter<VariableCollection>("Results", "The variable collection where results should be stored."));
Parameters.Add(new LookupParameter<IntValue>("EvaluatedMoves", "The number of evaluated moves."));
#endregion
#region Create operators
VariableCreator variableCreator = new VariableCreator();
SubScopesProcessor subScopesProcessor0 = new SubScopesProcessor();
Assigner bestQualityInitializer = new Assigner();
Placeholder analyzer1 = new Placeholder();
ResultsCollector resultsCollector1 = new ResultsCollector();
SubScopesProcessor solutionProcessor = new SubScopesProcessor();
Placeholder moveGenerator = new Placeholder();
UniformSubScopesProcessor moveEvaluationProcessor = new UniformSubScopesProcessor();
Placeholder moveEvaluator = new Placeholder();
Placeholder tabuChecker = new Placeholder();
SubScopesCounter subScopesCounter = new SubScopesCounter();
SubScopesSorter moveQualitySorter = new SubScopesSorter();
TabuSelector tabuSelector = new TabuSelector();
ConditionalBranch emptyNeighborhoodBranch1 = new ConditionalBranch();
SubScopesProcessor moveMakingProcessor = new SubScopesProcessor();
UniformSubScopesProcessor selectedMoveMakingProcesor = new UniformSubScopesProcessor();
Placeholder tabuMaker = new Placeholder();
Placeholder moveMaker = new Placeholder();
MergingReducer mergingReducer = new MergingReducer();
Placeholder analyzer2 = new Placeholder();
SubScopesRemover subScopesRemover = new SubScopesRemover();
ConditionalBranch emptyNeighborhoodBranch2 = new ConditionalBranch();
BestQualityMemorizer bestQualityUpdater = new BestQualityMemorizer();
IntCounter iterationsCounter = new IntCounter();
Comparator iterationsComparator = new Comparator();
ConditionalBranch iterationsTermination = new ConditionalBranch();
variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("Iterations", new IntValue(0))); // Class TabuSearch expects this to be called Iterations
variableCreator.CollectedValues.Add(new ValueParameter<BoolValue>("EmptyNeighborhood", new BoolValue(false)));
variableCreator.CollectedValues.Add(new ValueParameter<ItemList<IItem>>("TabuList", new ItemList<IItem>()));
variableCreator.CollectedValues.Add(new ValueParameter<VariableCollection>("Memories", new VariableCollection()));
variableCreator.CollectedValues.Add(new ValueParameter<DoubleValue>("BestQuality", new DoubleValue(0)));
bestQualityInitializer.Name = "Initialize BestQuality";
bestQualityInitializer.LeftSideParameter.ActualName = "BestQuality";
bestQualityInitializer.RightSideParameter.ActualName = QualityParameter.Name;
analyzer1.Name = "Analyzer (placeholder)";
analyzer1.OperatorParameter.ActualName = AnalyzerParameter.Name;
resultsCollector1.CopyValue = new BoolValue(false);
resultsCollector1.CollectedValues.Add(new LookupParameter<IntValue>("Iterations"));
resultsCollector1.CollectedValues.Add(new LookupParameter<DoubleValue>("Best Quality", null, "BestQuality"));
resultsCollector1.ResultsParameter.ActualName = ResultsParameter.Name;
moveGenerator.Name = "MoveGenerator (placeholder)";
moveGenerator.OperatorParameter.ActualName = MoveGeneratorParameter.Name;
moveEvaluationProcessor.Parallel = new BoolValue(true);
moveEvaluator.Name = "MoveEvaluator (placeholder)";
moveEvaluator.OperatorParameter.ActualName = MoveEvaluatorParameter.Name;
tabuChecker.Name = "TabuChecker (placeholder)";
tabuChecker.OperatorParameter.ActualName = TabuCheckerParameter.Name;
subScopesCounter.Name = "Increment EvaluatedMoves";
subScopesCounter.ValueParameter.ActualName = EvaluatedMovesParameter.Name;
moveQualitySorter.DescendingParameter.ActualName = MaximizationParameter.Name;
moveQualitySorter.ValueParameter.ActualName = MoveQualityParameter.Name;
tabuSelector.AspirationParameter.Value = new BoolValue(true);
tabuSelector.BestQualityParameter.ActualName = "BestQuality";
tabuSelector.CopySelected = new BoolValue(false);
tabuSelector.EmptyNeighborhoodParameter.ActualName = "EmptyNeighborhood";
tabuSelector.MaximizationParameter.ActualName = MaximizationParameter.Name;
tabuSelector.MoveQualityParameter.ActualName = MoveQualityParameter.Name;
tabuSelector.MoveTabuParameter.ActualName = MoveTabuParameter.Name;
moveMakingProcessor.Name = "MoveMaking processor (UniformSubScopesProcessor)";
emptyNeighborhoodBranch1.Name = "Neighborhood empty?";
emptyNeighborhoodBranch1.ConditionParameter.ActualName = "EmptyNeighborhood";
tabuMaker.Name = "TabuMaker (placeholder)";
tabuMaker.OperatorParameter.ActualName = TabuMakerParameter.Name;
moveMaker.Name = "MoveMaker (placeholder)";
moveMaker.OperatorParameter.ActualName = MoveMakerParameter.Name;
analyzer2.Name = "Analyzer (placeholder)";
analyzer2.OperatorParameter.ActualName = AnalyzerParameter.Name;
subScopesRemover.RemoveAllSubScopes = true;
bestQualityUpdater.Name = "Update BestQuality";
bestQualityUpdater.MaximizationParameter.ActualName = MaximizationParameter.Name;
bestQualityUpdater.QualityParameter.ActualName = QualityParameter.Name;
bestQualityUpdater.BestQualityParameter.ActualName = "BestQuality";
iterationsCounter.Name = "Iterations Counter";
iterationsCounter.Increment = new IntValue(1);
iterationsCounter.ValueParameter.ActualName = "Iterations";
iterationsComparator.Name = "Iterations >= MaximumIterations";
iterationsComparator.Comparison = new Comparison(ComparisonType.GreaterOrEqual);
iterationsComparator.LeftSideParameter.ActualName = "Iterations";
iterationsComparator.RightSideParameter.ActualName = MaximumIterationsParameter.Name;
iterationsComparator.ResultParameter.ActualName = "Terminate";
emptyNeighborhoodBranch2.Name = "Neighborhood empty?";
emptyNeighborhoodBranch2.ConditionParameter.ActualName = "EmptyNeighborhood";
iterationsTermination.Name = "Iterations Termination Condition";
iterationsTermination.ConditionParameter.ActualName = "Terminate";
#endregion
#region Create operator graph
OperatorGraph.InitialOperator = variableCreator;
variableCreator.Successor = subScopesProcessor0;
subScopesProcessor0.Operators.Add(bestQualityInitializer);
subScopesProcessor0.Successor = resultsCollector1;
bestQualityInitializer.Successor = analyzer1;
analyzer1.Successor = null;
resultsCollector1.Successor = solutionProcessor;
solutionProcessor.Operators.Add(moveGenerator);
solutionProcessor.Successor = iterationsCounter;
moveGenerator.Successor = moveEvaluationProcessor;
moveEvaluationProcessor.Operator = moveEvaluator;
moveEvaluationProcessor.Successor = subScopesCounter;
moveEvaluator.Successor = tabuChecker;
tabuChecker.Successor = null;
subScopesCounter.Successor = moveQualitySorter;
moveQualitySorter.Successor = tabuSelector;
tabuSelector.Successor = emptyNeighborhoodBranch1;
emptyNeighborhoodBranch1.FalseBranch = moveMakingProcessor;
emptyNeighborhoodBranch1.TrueBranch = null;
emptyNeighborhoodBranch1.Successor = subScopesRemover;
moveMakingProcessor.Operators.Add(new EmptyOperator());
moveMakingProcessor.Operators.Add(selectedMoveMakingProcesor);
moveMakingProcessor.Successor = mergingReducer;
selectedMoveMakingProcesor.Operator = tabuMaker;
selectedMoveMakingProcesor.Successor = null;
tabuMaker.Successor = moveMaker;
moveMaker.Successor = null;
mergingReducer.Successor = analyzer2;
analyzer2.Successor = null;
subScopesRemover.Successor = null;
iterationsCounter.Successor = iterationsComparator;
iterationsComparator.Successor = emptyNeighborhoodBranch2;
emptyNeighborhoodBranch2.TrueBranch = null;
emptyNeighborhoodBranch2.FalseBranch = iterationsTermination;
emptyNeighborhoodBranch2.Successor = null;
iterationsTermination.TrueBranch = null;
iterationsTermination.FalseBranch = solutionProcessor;
#endregion
}
private CMAEvolutionStrategy(CMAEvolutionStrategy original, Cloner cloner)
: base(original, cloner) {
qualityAnalyzer = cloner.Clone(original.qualityAnalyzer);
cmaAnalyzer = cloner.Clone(original.cmaAnalyzer);
solutionCreator = cloner.Clone(original.solutionCreator);
populationSolutionCreator = cloner.Clone(original.populationSolutionCreator);
evaluator = cloner.Clone(original.evaluator);
sorter = cloner.Clone(original.sorter);
terminator = cloner.Clone(original.terminator);
RegisterEventHandlers();
}
private SubScopesSorter(SubScopesSorter original, Cloner cloner)
: base(original, cloner) {
}
