public MQClientCounters(string name, bool ignoreCounters)
{
if (ignoreCounters)
{
return;
}
_messagesSentCount = Core.Counters.GetIntegerCounter(Category, name + @"\Messages Sent", CounterType.Cumulative, CounterLevel.Framework, CounterKind.Messaging, CounterUnit.Messages);
_messagesReceivedCount = Core.Counters.GetIntegerCounter(Category, name + @"\Messages Received", CounterType.Cumulative, CounterLevel.Framework, CounterKind.Messaging, CounterUnit.Messages);
_receptionTime = Core.Counters.GetDoubleCounter(Category, name + @"\Reception Time", CounterType.Average, CounterLevel.Framework, CounterKind.Messaging, CounterUnit.Milliseconds);
_bytesSent = Core.Counters.GetIntegerCounter(Category, name + @"\Bytes Sent", CounterType.Cumulative, CounterLevel.Framework, CounterKind.Messaging, CounterUnit.Bytes);
_bytesReceived = Core.Counters.GetIntegerCounter(Category, name + @"\Bytes Received", CounterType.Cumulative, CounterLevel.Framework, CounterKind.Messaging, CounterUnit.Bytes);
}
public MQServerCounters(string name, bool ignoreCounters)
{
if (ignoreCounters)
{
return;
}
_currentMessages = Core.Counters.GetIntegerCounter(Category, name + @"\Current Messages", CounterType.Current, CounterLevel.Framework, CounterKind.Messaging, CounterUnit.Messages);
_totalMessagesReceived = Core.Counters.GetIntegerCounter(Category, name + @"\Messages Received", CounterType.Cumulative, CounterLevel.Framework, CounterKind.Messaging, CounterUnit.Messages);
_totalMessagesProcessed = Core.Counters.GetIntegerCounter(Category, name + @"\Messages Processed", CounterType.Cumulative, CounterLevel.Framework, CounterKind.Messaging, CounterUnit.Messages);
_totalExceptions = Core.Counters.GetIntegerCounter(Category, name + @"\Exceptions", CounterType.Cumulative, CounterLevel.Framework, CounterKind.Messaging, CounterUnit.Exceptions);
_totalReceivingTime = Core.Counters.GetDoubleCounter(Category, name + @"\Receiving Time", CounterType.Average, CounterLevel.Framework, CounterKind.Messaging, CounterUnit.Milliseconds);
_totalReceivingBytes = Core.Counters.GetIntegerCounter(Category, name + @"\Receiving Bytes", CounterType.Cumulative, CounterLevel.Framework, CounterKind.Messaging, CounterUnit.Bytes);
}
public CacheClientPoolCounters(string name)
{
_existKeyTime = Core.Counters.GetDoubleCounter(Category, name + @"\Exists Time", CounterType.Average, CounterLevel.Framework, CounterKind.Cache, CounterUnit.Milliseconds);
_getTime = Core.Counters.GetDoubleCounter(Category, name + @"\Get Time", CounterType.Average, CounterLevel.Framework, CounterKind.Cache, CounterUnit.Milliseconds);
_getByTagTime = Core.Counters.GetDoubleCounter(Category, name + @"\Get By Tag Time", CounterType.Average, CounterLevel.Framework, CounterKind.Cache, CounterUnit.Milliseconds);
_getCreationDateTime = Core.Counters.GetDoubleCounter(Category, name + @"\Get CreationDate Time", CounterType.Average, CounterLevel.Framework, CounterKind.Cache, CounterUnit.Milliseconds);
_getExpirationDateTime = Core.Counters.GetDoubleCounter(Category, name + @"\Get ExpirationDate Time", CounterType.Average, CounterLevel.Framework, CounterKind.Cache, CounterUnit.Milliseconds);
_getKeysTime = Core.Counters.GetDoubleCounter(Category, name + @"\Get Keys Time", CounterType.Average, CounterLevel.Framework, CounterKind.Cache, CounterUnit.Milliseconds);
_getMetaTime = Core.Counters.GetDoubleCounter(Category, name + @"\Get Meta Time", CounterType.Average, CounterLevel.Framework, CounterKind.Cache, CounterUnit.Milliseconds);
_getMetaByTagTime = Core.Counters.GetDoubleCounter(Category, name + @"\Get Meta By Tag Time", CounterType.Average, CounterLevel.Framework, CounterKind.Cache, CounterUnit.Milliseconds);
_getOrSetTime = Core.Counters.GetDoubleCounter(Category, name + @"\Get Or Sets Time", CounterType.Average, CounterLevel.Framework, CounterKind.Cache, CounterUnit.Milliseconds);
_removeTime = Core.Counters.GetDoubleCounter(Category, name + @"\Remove Time", CounterType.Average, CounterLevel.Framework, CounterKind.Cache, CounterUnit.Milliseconds);
_removeByTagTime = Core.Counters.GetDoubleCounter(Category, name + @"\Remove By Tag Time", CounterType.Average, CounterLevel.Framework, CounterKind.Cache, CounterUnit.Milliseconds);
_setTime = Core.Counters.GetDoubleCounter(Category, name + @"\Set Time", CounterType.Average, CounterLevel.Framework, CounterKind.Cache, CounterUnit.Milliseconds);
_updateDataTime = Core.Counters.GetDoubleCounter(Category, name + @"\Update Data Time", CounterType.Average, CounterLevel.Framework, CounterKind.Cache, CounterUnit.Milliseconds);
_copyTime = Core.Counters.GetDoubleCounter(Category, name + @"\Copy Time", CounterType.Average, CounterLevel.Framework, CounterKind.Cache, CounterUnit.Milliseconds);
_setMultiTime = Core.Counters.GetDoubleCounter(Category, name + @"\Set Multi Time", CounterType.Average, CounterLevel.Framework, CounterKind.Cache, CounterUnit.Milliseconds);
_executeExtensionTime = Core.Counters.GetDoubleCounter(Category, name + @"\Execute Extension Time", CounterType.Average, CounterLevel.Framework, CounterKind.Cache, CounterUnit.Milliseconds);
}
public DoubleCounterTest()
{
counter = new DoubleCounter(MonitorConfig.Build("Test"));
}
private void Initialize()
{
Parameters.Add(new ValueLookupParameter <IRandom>("Random", "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 LookupParameter <IntValue>("EvaluatedSolutions", "The number of evaluated solutions."));
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 <IntValue>("PopulationSize", "The size of the population of solutions in each layer."));
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 LookupParameter <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 LookupParameter <DoubleValue>("CurrentSuccessRatio", "The current success ratio."));
Parameters.Add(new ValueLookupParameter <DoubleValue>("SuccessRatio", "The ratio of successful to total children that should be achieved."));
Parameters.Add(new LookupParameter <DoubleValue>("SelectionPressure", "The actual selection pressure."));
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 <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 <DoubleValue>("AgeIncrement", "The value the age the individuals is incremented if they survives a generation."));
var selector = new Placeholder();
var subScopesProcessor1 = new SubScopesProcessor();
var childrenCreator = new ChildrenCreator();
var osBeforeMutationBranch = new ConditionalBranch();
var uniformSubScopesProcessor1 = new UniformSubScopesProcessor();
var crossover1 = new Placeholder();
var uniformSubScopesProcessor2 = new UniformSubScopesProcessor();
var evaluator1 = new Placeholder();
var subScopesCounter1 = new SubScopesCounter();
var qualityComparer1 = new WeightedParentsQualityComparator();
var ageCalculator1 = new WeightingReducer()
{
Name = "Calculate Age"
};
var subScopesRemover1 = new SubScopesRemover();
var uniformSubScopesProcessor3 = new UniformSubScopesProcessor();
var mutationBranch1 = new StochasticBranch();
var mutator1 = new Placeholder();
var variableCreator1 = new VariableCreator();
var variableCreator2 = new VariableCreator();
var conditionalSelector = new ConditionalSelector();
var subScopesProcessor2 = new SubScopesProcessor();
var uniformSubScopesProcessor4 = new UniformSubScopesProcessor();
var evaluator2 = new Placeholder();
var subScopesCounter2 = new SubScopesCounter();
var mergingReducer1 = new MergingReducer();
var uniformSubScopesProcessor5 = new UniformSubScopesProcessor();
var crossover2 = new Placeholder();
var mutationBranch2 = new StochasticBranch();
var mutator2 = new Placeholder();
var uniformSubScopesProcessor6 = new UniformSubScopesProcessor();
var evaluator3 = new Placeholder();
var subScopesCounter3 = new SubScopesCounter();
var qualityComparer2 = new WeightedParentsQualityComparator();
var ageCalculator2 = new WeightingReducer()
{
Name = "Calculate Age"
};
var subScopesRemover2 = new SubScopesRemover();
var offspringSelector = new AlpsOffspringSelector();
var subScopesProcessor3 = new SubScopesProcessor();
var bestSelector = new BestSelector();
var worstSelector = new WorstSelector();
var rightReducer = new RightReducer();
var leftReducer = new LeftReducer();
var mergingReducer2 = new MergingReducer();
var reevaluateElitesBranch = new ConditionalBranch();
var uniformSubScopesProcessor7 = new UniformSubScopesProcessor();
var evaluator4 = new Placeholder();
var subScopesCounter4 = new SubScopesCounter();
var incrementAgeProcessor = new UniformSubScopesProcessor();
var ageIncrementor = new DoubleCounter()
{
Name = "Increment Age"
};
OperatorGraph.InitialOperator = selector;
selector.Name = "Selector (placeholder)";
selector.OperatorParameter.ActualName = SelectorParameter.Name;
selector.Successor = subScopesProcessor1;
subScopesProcessor1.Operators.Add(new EmptyOperator());
subScopesProcessor1.Operators.Add(childrenCreator);
subScopesProcessor1.Successor = offspringSelector;
childrenCreator.ParentsPerChild = new IntValue(2);
childrenCreator.Successor = osBeforeMutationBranch;
osBeforeMutationBranch.Name = "Apply OS before mutation?";
osBeforeMutationBranch.ConditionParameter.ActualName = OffspringSelectionBeforeMutationParameter.Name;
osBeforeMutationBranch.TrueBranch = uniformSubScopesProcessor1;
osBeforeMutationBranch.FalseBranch = uniformSubScopesProcessor5;
osBeforeMutationBranch.Successor = null;
uniformSubScopesProcessor1.Operator = crossover1;
uniformSubScopesProcessor1.Successor = uniformSubScopesProcessor2;
crossover1.Name = "Crossover (placeholder)";
crossover1.OperatorParameter.ActualName = CrossoverParameter.Name;
crossover1.Successor = null;
uniformSubScopesProcessor2.Parallel.Value = true;
uniformSubScopesProcessor2.Operator = evaluator1;
uniformSubScopesProcessor2.Successor = subScopesCounter1;
evaluator1.Name = "Evaluator (placeholder)";
evaluator1.OperatorParameter.ActualName = EvaluatorParameter.Name;
evaluator1.Successor = qualityComparer1;
subScopesCounter1.Name = "Increment EvaluatedSolutions";
subScopesCounter1.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
subScopesCounter1.Successor = uniformSubScopesProcessor3;
uniformSubScopesProcessor3.Operator = mutationBranch1;
uniformSubScopesProcessor3.Successor = conditionalSelector;
qualityComparer1.ComparisonFactorParameter.ActualName = ComparisonFactorParameter.Name;
qualityComparer1.LeftSideParameter.ActualName = QualityParameter.Name;
qualityComparer1.MaximizationParameter.ActualName = MaximizationParameter.Name;
qualityComparer1.RightSideParameter.ActualName = QualityParameter.Name;
qualityComparer1.ResultParameter.ActualName = "SuccessfulOffspring";
qualityComparer1.Successor = ageCalculator1;
ageCalculator1.ParameterToReduce.ActualName = AgeParameter.Name;
ageCalculator1.TargetParameter.ActualName = AgeParameter.Name;
ageCalculator1.WeightParameter.ActualName = AgeInheritanceParameter.Name;
ageCalculator1.Successor = subScopesRemover1;
subScopesRemover1.RemoveAllSubScopes = true;
subScopesRemover1.Successor = null;
mutationBranch1.ProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
mutationBranch1.RandomParameter.ActualName = RandomParameter.Name;
mutationBranch1.FirstBranch = mutator1;
mutationBranch1.SecondBranch = variableCreator2;
mutationBranch1.Successor = null;
mutator1.Name = "Mutator (placeholder)";
mutator1.OperatorParameter.ActualName = MutatorParameter.Name;
mutator1.Successor = variableCreator1;
variableCreator1.Name = "MutatedOffspring = true";
variableCreator1.CollectedValues.Add(new ValueParameter <BoolValue>("MutatedOffspring", null, new BoolValue(true), false));
variableCreator1.Successor = null;
variableCreator2.Name = "MutatedOffspring = false";
variableCreator2.CollectedValues.Add(new ValueParameter <BoolValue>("MutatedOffspring", null, new BoolValue(false), false));
variableCreator2.Successor = null;
conditionalSelector.ConditionParameter.ActualName = "MutatedOffspring";
conditionalSelector.ConditionParameter.Depth = 1;
conditionalSelector.CopySelected.Value = false;
conditionalSelector.Successor = subScopesProcessor2;
subScopesProcessor2.Operators.Add(new EmptyOperator());
subScopesProcessor2.Operators.Add(uniformSubScopesProcessor4);
subScopesProcessor2.Successor = mergingReducer1;
mergingReducer1.Successor = null;
uniformSubScopesProcessor4.Parallel.Value = true;
uniformSubScopesProcessor4.Operator = evaluator2;
uniformSubScopesProcessor4.Successor = subScopesCounter2;
evaluator2.Name = "Evaluator (placeholder)";
evaluator2.OperatorParameter.ActualName = EvaluatorParameter.Name;
evaluator2.Successor = null;
subScopesCounter2.Name = "Increment EvaluatedSolutions";
subScopesCounter2.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
subScopesCounter2.Successor = null;
uniformSubScopesProcessor5.Operator = crossover2;
uniformSubScopesProcessor5.Successor = uniformSubScopesProcessor6;
crossover2.Name = "Crossover (placeholder)";
crossover2.OperatorParameter.ActualName = CrossoverParameter.Name;
crossover2.Successor = mutationBranch2;
mutationBranch2.ProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
mutationBranch2.RandomParameter.ActualName = RandomParameter.Name;
mutationBranch2.FirstBranch = mutator2;
mutationBranch2.SecondBranch = null;
mutationBranch2.Successor = null;
mutator2.Name = "Mutator (placeholder)";
mutator2.OperatorParameter.ActualName = MutatorParameter.Name;
mutator2.Successor = null;
uniformSubScopesProcessor6.Parallel.Value = true;
uniformSubScopesProcessor6.Operator = evaluator3;
uniformSubScopesProcessor6.Successor = subScopesCounter3;
evaluator3.Name = "Evaluator (placeholder)";
evaluator3.OperatorParameter.ActualName = EvaluatorParameter.Name;
evaluator3.Successor = qualityComparer2;
subScopesCounter3.Name = "Increment EvaluatedSolutions";
subScopesCounter3.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
qualityComparer2.ComparisonFactorParameter.ActualName = ComparisonFactorParameter.Name;
qualityComparer2.LeftSideParameter.ActualName = QualityParameter.Name;
qualityComparer2.MaximizationParameter.ActualName = MaximizationParameter.Name;
qualityComparer2.RightSideParameter.ActualName = QualityParameter.Name;
qualityComparer2.ResultParameter.ActualName = "SuccessfulOffspring";
qualityComparer2.Successor = ageCalculator2;
ageCalculator2.ParameterToReduce.ActualName = AgeParameter.Name;
ageCalculator2.TargetParameter.ActualName = AgeParameter.Name;
ageCalculator2.WeightParameter.ActualName = AgeInheritanceParameter.Name;
ageCalculator2.Successor = subScopesRemover2;
subScopesRemover2.RemoveAllSubScopes = true;
subScopesRemover2.Successor = null;
subScopesCounter3.Successor = null;
offspringSelector.CurrentSuccessRatioParameter.ActualName = CurrentSuccessRatioParameter.Name;
offspringSelector.MaximumSelectionPressureParameter.ActualName = MaximumSelectionPressureParameter.Name;
offspringSelector.SelectionPressureParameter.ActualName = SelectionPressureParameter.Name;
offspringSelector.SuccessRatioParameter.ActualName = SuccessRatioParameter.Name;
offspringSelector.OffspringPopulationParameter.ActualName = "OffspringPopulation";
offspringSelector.OffspringPopulationWinnersParameter.ActualName = "OffspringPopulationWinners";
offspringSelector.SuccessfulOffspringParameter.ActualName = "SuccessfulOffspring";
offspringSelector.FillPopulationWithParentsParameter.ActualName = FillPopulationWithParentsParameter.Name;
offspringSelector.PopulationSizeParameter.ActualName = PopulationSizeParameter.Name;
offspringSelector.OffspringCreator = selector;
offspringSelector.Successor = subScopesProcessor3;
subScopesProcessor3.Operators.Add(bestSelector);
subScopesProcessor3.Operators.Add(worstSelector);
subScopesProcessor3.Successor = mergingReducer2;
bestSelector.CopySelected = new BoolValue(false);
bestSelector.MaximizationParameter.ActualName = MaximizationParameter.Name;
bestSelector.NumberOfSelectedSubScopesParameter.ActualName = ElitesParameter.Name;
bestSelector.QualityParameter.ActualName = QualityParameter.Name;
bestSelector.Successor = rightReducer;
rightReducer.Successor = reevaluateElitesBranch;
reevaluateElitesBranch.ConditionParameter.ActualName = "ReevaluateElites";
reevaluateElitesBranch.Name = "Reevaluate elites ?";
reevaluateElitesBranch.TrueBranch = uniformSubScopesProcessor7;
reevaluateElitesBranch.FalseBranch = null;
reevaluateElitesBranch.Successor = null;
uniformSubScopesProcessor7.Parallel.Value = true;
uniformSubScopesProcessor7.Operator = evaluator4;
uniformSubScopesProcessor7.Successor = subScopesCounter4;
evaluator4.Name = "Evaluator (placeholder)";
evaluator4.OperatorParameter.ActualName = EvaluatorParameter.Name;
subScopesCounter4.Name = "Increment EvaluatedSolutions";
subScopesCounter4.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
subScopesCounter4.Successor = null;
worstSelector.CopySelected = new BoolValue(false);
worstSelector.MaximizationParameter.ActualName = MaximizationParameter.Name;
worstSelector.NumberOfSelectedSubScopesParameter.ActualName = ElitesParameter.Name;
worstSelector.QualityParameter.ActualName = QualityParameter.Name;
worstSelector.Successor = leftReducer;
leftReducer.Successor = null;
mergingReducer2.Successor = incrementAgeProcessor;
incrementAgeProcessor.Operator = ageIncrementor;
incrementAgeProcessor.Successor = null;
ageIncrementor.ValueParameter.ActualName = AgeParameter.Name;
ageIncrementor.IncrementParameter.Value = null;
ageIncrementor.IncrementParameter.ActualName = AgeIncrementParameter.Name;
ageIncrementor.Successor = null;
}
public AlpsGeneticAlgorithmMainOperator()
: base()
{
Parameters.Add(new ValueLookupParameter <IRandom>("Random", "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 <IntValue>("PopulationSize", "The size of the population of solutions in each layer."));
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 <BoolValue>("PlusSelection", "Include the parents in the selection of the invividuals for the next generation."));
Parameters.Add(new ScopeTreeLookupParameter <DoubleValue>("Age", "The age of individuals."));
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 <DoubleValue>("AgeIncrement", "The value the age the individuals is incremented if they survives a generation."));
var numberOfSelectedParentsCalculator = new ExpressionCalculator()
{
Name = "NumberOfSelectedParents = 2 * (PopulationSize - (PlusSelection ? 0 : Elites))"
};
var selector = new Placeholder()
{
Name = "Selector (Placeholder)"
};
var subScopesProcessor1 = new SubScopesProcessor();
var childrenCreator = new ChildrenCreator();
var uniformSubScopesProcessor1 = new UniformSubScopesProcessor();
var crossover = new Placeholder()
{
Name = "Crossover (Placeholder)"
};
var stochasticBranch = new StochasticBranch()
{
Name = "MutationProbability"
};
var mutator = new Placeholder()
{
Name = "Mutator (Placeholder)"
};
var ageCalculator = new WeightingReducer()
{
Name = "Calculate Age"
};
var subScopesRemover = new SubScopesRemover();
var uniformSubScopesProcessor2 = new UniformSubScopesProcessor();
var evaluator = new Placeholder()
{
Name = "Evaluator (Placeholder)"
};
var subScopesCounter = new SubScopesCounter()
{
Name = "Increment EvaluatedSolutions"
};
var replacementBranch = new ConditionalBranch()
{
Name = "PlusSelection?"
};
var replacementMergingReducer = new MergingReducer();
var replacementBestSelector = new BestSelector();
var replacementRightReducer = new RightReducer();
var subScopesProcessor2 = new SubScopesProcessor();
var bestSelector = new BestSelector();
var rightReducer = new RightReducer();
var mergingReducer = new MergingReducer();
var reevaluateElitesBranch = new ConditionalBranch()
{
Name = "Reevaluate elites ?"
};
var incrementAgeProcessor = new UniformSubScopesProcessor();
var ageIncrementor = new DoubleCounter()
{
Name = "Increment Age"
};
OperatorGraph.InitialOperator = numberOfSelectedParentsCalculator;
numberOfSelectedParentsCalculator.CollectedValues.Add(new LookupParameter <IntValue>(PopulationSizeParameter.Name));
numberOfSelectedParentsCalculator.CollectedValues.Add(new LookupParameter <IntValue>(ElitesParameter.Name));
numberOfSelectedParentsCalculator.CollectedValues.Add(new LookupParameter <BoolValue>(PlusSelectionParameter.Name));
numberOfSelectedParentsCalculator.ExpressionResultParameter.ActualName = "NumberOfSelectedSubScopes";
numberOfSelectedParentsCalculator.ExpressionParameter.Value = new StringValue("PopulationSize 0 Elites PlusSelection if - 2 * toint");
numberOfSelectedParentsCalculator.Successor = selector;
selector.OperatorParameter.ActualName = SelectorParameter.Name;
selector.Successor = subScopesProcessor1;
subScopesProcessor1.Operators.Add(new EmptyOperator());
subScopesProcessor1.Operators.Add(childrenCreator);
subScopesProcessor1.Successor = replacementBranch;
childrenCreator.ParentsPerChild = new IntValue(2);
childrenCreator.Successor = uniformSubScopesProcessor1;
uniformSubScopesProcessor1.Operator = crossover;
uniformSubScopesProcessor1.Successor = uniformSubScopesProcessor2;
crossover.OperatorParameter.ActualName = CrossoverParameter.Name;
crossover.Successor = stochasticBranch;
stochasticBranch.ProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
stochasticBranch.RandomParameter.ActualName = RandomParameter.Name;
stochasticBranch.FirstBranch = mutator;
stochasticBranch.SecondBranch = null;
stochasticBranch.Successor = ageCalculator;
mutator.OperatorParameter.ActualName = MutatorParameter.Name;
mutator.Successor = null;
ageCalculator.ParameterToReduce.ActualName = AgeParameter.Name;
ageCalculator.TargetParameter.ActualName = AgeParameter.Name;
ageCalculator.WeightParameter.ActualName = AgeInheritanceParameter.Name;
ageCalculator.Successor = subScopesRemover;
subScopesRemover.RemoveAllSubScopes = true;
subScopesRemover.Successor = null;
uniformSubScopesProcessor2.Parallel.Value = true;
uniformSubScopesProcessor2.Operator = evaluator;
uniformSubScopesProcessor2.Successor = subScopesCounter;
evaluator.OperatorParameter.ActualName = EvaluatorParameter.Name;
evaluator.Successor = null;
subScopesCounter.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
subScopesCounter.AccumulateParameter.Value = new BoolValue(true);
subScopesCounter.Successor = null;
replacementBranch.ConditionParameter.ActualName = PlusSelectionParameter.Name;
replacementBranch.TrueBranch = replacementMergingReducer;
replacementBranch.FalseBranch = subScopesProcessor2;
replacementBranch.Successor = incrementAgeProcessor;
replacementMergingReducer.Successor = replacementBestSelector;
replacementBestSelector.NumberOfSelectedSubScopesParameter.ActualName = PopulationSizeParameter.Name;
replacementBestSelector.CopySelected = new BoolValue(false);
replacementBestSelector.Successor = replacementRightReducer;
replacementRightReducer.Successor = reevaluateElitesBranch;
subScopesProcessor2.Operators.Add(bestSelector);
subScopesProcessor2.Operators.Add(new EmptyOperator());
subScopesProcessor2.Successor = mergingReducer;
bestSelector.CopySelected = new BoolValue(false);
bestSelector.MaximizationParameter.ActualName = MaximizationParameter.Name;
bestSelector.NumberOfSelectedSubScopesParameter.ActualName = ElitesParameter.Name;
bestSelector.QualityParameter.ActualName = QualityParameter.Name;
bestSelector.Successor = rightReducer;
rightReducer.Successor = reevaluateElitesBranch;
mergingReducer.Successor = null;
reevaluateElitesBranch.ConditionParameter.ActualName = ReevaluateElitesParameter.Name;
reevaluateElitesBranch.TrueBranch = uniformSubScopesProcessor2;
reevaluateElitesBranch.FalseBranch = null;
reevaluateElitesBranch.Successor = null;
incrementAgeProcessor.Operator = ageIncrementor;
incrementAgeProcessor.Successor = null;
ageIncrementor.ValueParameter.ActualName = AgeParameter.Name;
ageIncrementor.IncrementParameter.Value = null;
ageIncrementor.IncrementParameter.ActualName = AgeIncrementParameter.Name;
ageIncrementor.Successor = null;
}
public AlpsNsga2MainOperator() : base()
{
Parameters.Add(new ValueLookupParameter <IRandom>("Random", "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 <IntValue>("PopulationSize", "The size of the population of solutions in each layer."));
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 <PercentValue>("CrossoverProbability", "The probability that the crossover operator is applied on a solution."));
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 ScopeTreeLookupParameter <DoubleValue>("Age", "The age of individuals."));
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 <DoubleValue>("AgeIncrement", "The value the age the individuals is incremented if they survives a generation."));
Parameters.Add(new ValueLookupParameter <BoolValue>("DominateOnEqualQualities", "Flag which determines whether solutions with equal quality values should be treated as dominated."));
var selector = new Placeholder {
Name = "Selector (Placeholder)"
};
var subScopesProcessor1 = new SubScopesProcessor();
var childrenCreator = new ChildrenCreator();
var uniformSubScopesProcessor1 = new UniformSubScopesProcessor();
var crossoverStochasticBranch = new StochasticBranch {
Name = "CrossoverProbability"
};
var crossover = new Placeholder {
Name = "Crossover (Placeholder)"
};
var noCrossover = new ParentCopyCrossover();
var mutationStochasticBranch = new StochasticBranch {
Name = "MutationProbability"
};
var mutator = new Placeholder {
Name = "Mutator (Placeholder)"
};
var ageCalculator = new WeightingReducer {
Name = "Calculate Age"
};
var subScopesRemover = new SubScopesRemover();
var uniformSubScopesProcessor2 = new UniformSubScopesProcessor();
var evaluator = new Placeholder {
Name = "Evaluator (Placeholder)"
};
var subScopesCounter = new SubScopesCounter {
Name = "Increment EvaluatedSolutions"
};
var mergingReducer = new MergingReducer();
var rankAndCrowdingSorter1 = new RankAndCrowdingSorter();
var rankAndCrowdingSorter2 = new RankAndCrowdingSorter();
var leftSelector = new LeftSelector();
var rightReducer = new RightReducer();
var incrementAgeProcessor = new UniformSubScopesProcessor();
var ageIncrementer = new DoubleCounter {
Name = "Increment Age"
};
OperatorGraph.InitialOperator = rankAndCrowdingSorter1;
rankAndCrowdingSorter1.DominateOnEqualQualitiesParameter.ActualName = DominateOnEqualQualitiesParameter.Name;
rankAndCrowdingSorter1.CrowdingDistanceParameter.ActualName = "CrowdingDistance";
rankAndCrowdingSorter1.RankParameter.ActualName = "Rank";
rankAndCrowdingSorter1.Successor = selector;
selector.OperatorParameter.ActualName = SelectorParameter.Name;
selector.Successor = subScopesProcessor1;
subScopesProcessor1.Operators.Add(new EmptyOperator());
subScopesProcessor1.Operators.Add(childrenCreator);
subScopesProcessor1.Successor = mergingReducer;
childrenCreator.ParentsPerChild = new IntValue(2);
childrenCreator.Successor = uniformSubScopesProcessor1;
uniformSubScopesProcessor1.Operator = crossoverStochasticBranch;
uniformSubScopesProcessor1.Successor = uniformSubScopesProcessor2;
crossoverStochasticBranch.ProbabilityParameter.ActualName = CrossoverProbabilityParameter.Name;
crossoverStochasticBranch.RandomParameter.ActualName = RandomParameter.Name;
crossoverStochasticBranch.FirstBranch = crossover;
crossoverStochasticBranch.SecondBranch = noCrossover;
crossoverStochasticBranch.Successor = mutationStochasticBranch;
crossover.Name = "Crossover";
crossover.OperatorParameter.ActualName = CrossoverParameter.Name;
crossover.Successor = null;
noCrossover.Name = "Clone parent";
noCrossover.RandomParameter.ActualName = RandomParameter.Name;
noCrossover.Successor = null;
mutationStochasticBranch.ProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
mutationStochasticBranch.RandomParameter.ActualName = RandomParameter.Name;
mutationStochasticBranch.FirstBranch = mutator;
mutationStochasticBranch.SecondBranch = null;
mutationStochasticBranch.Successor = ageCalculator;
mutator.Name = "Mutator";
mutator.OperatorParameter.ActualName = MutatorParameter.Name;
mutator.Successor = null;
ageCalculator.ParameterToReduce.ActualName = AgeParameter.Name;
ageCalculator.TargetParameter.ActualName = AgeParameter.Name;
ageCalculator.WeightParameter.ActualName = AgeInheritanceParameter.Name;
ageCalculator.Successor = subScopesRemover;
subScopesRemover.RemoveAllSubScopes = true;
subScopesRemover.Successor = null;
uniformSubScopesProcessor2.Parallel.Value = true;
uniformSubScopesProcessor2.Operator = evaluator;
uniformSubScopesProcessor2.Successor = subScopesCounter;
evaluator.OperatorParameter.ActualName = EvaluatorParameter.Name;
evaluator.Successor = null;
subScopesCounter.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
subScopesCounter.AccumulateParameter.Value = new BoolValue(true);
subScopesCounter.Successor = null;
mergingReducer.Successor = rankAndCrowdingSorter2;
rankAndCrowdingSorter2.DominateOnEqualQualitiesParameter.ActualName = DominateOnEqualQualitiesParameter.Name;
rankAndCrowdingSorter2.CrowdingDistanceParameter.ActualName = "CrowdingDistance";
rankAndCrowdingSorter2.RankParameter.ActualName = "Rank";
rankAndCrowdingSorter2.Successor = leftSelector;
leftSelector.CopySelected = new BoolValue(false);
leftSelector.NumberOfSelectedSubScopesParameter.ActualName = PopulationSizeParameter.Name;
leftSelector.Successor = rightReducer;
rightReducer.Successor = incrementAgeProcessor;
incrementAgeProcessor.Operator = ageIncrementer;
incrementAgeProcessor.Successor = null;
ageIncrementer.ValueParameter.ActualName = AgeParameter.Name;
ageIncrementer.IncrementParameter.Value = null;
ageIncrementer.IncrementParameter.ActualName = AgeIncrementParameter.Name;
ageIncrementer.Successor = null;
}