public AlpsGeneticAlgorithm()
: base()
{
#region Add parameters
Parameters.Add(new ValueParameter <IntValue>("Seed", "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
Parameters.Add(new ValueParameter <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 all individuals from all layers combined.", new MultiAnalyzer()));
Parameters.Add(new FixedValueParameter <MultiAnalyzer>("LayerAnalyzer", "The operator used to analyze each layer.", new MultiAnalyzer()));
Parameters.Add(new ValueParameter <IntValue>("NumberOfLayers", "The number of layers.", new IntValue(10)));
Parameters.Add(new ValueParameter <IntValue>("PopulationSize", "The size of the population of solutions in each layer.", new IntValue(100)));
Parameters.Add(new ConstrainedValueParameter <ISelector>("Selector", "The operator used to select solutions for reproduction."));
Parameters.Add(new ConstrainedValueParameter <ICrossover>("Crossover", "The operator used to cross solutions."));
Parameters.Add(new ConstrainedValueParameter <IManipulator>("Mutator", "The operator used to mutate solutions."));
Parameters.Add(new ValueParameter <PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution.", new PercentValue(0.05)));
Parameters.Add(new ValueParameter <IntValue>("Elites", "The numer of elite solutions which are kept in each generation.", new IntValue(1)));
Parameters.Add(new FixedValueParameter <BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)", new BoolValue(false))
{
Hidden = true
});
Parameters.Add(new ValueParameter <BoolValue>("PlusSelection", "Include the parents in the selection of the invividuals for the next generation.", new BoolValue(false)));
Parameters.Add(new ValueParameter <EnumValue <AgingScheme> >("AgingScheme", "The aging scheme for setting the age-limits for the layers.", new EnumValue <AgingScheme>(ALPS.AgingScheme.Polynomial)));
Parameters.Add(new ValueParameter <IntValue>("AgeGap", "The frequency of reseeding the lowest layer and scaling factor for the age-limits for the layers.", new IntValue(20)));
Parameters.Add(new ValueParameter <DoubleValue>("AgeInheritance", "A weight that determines the age of a child after crossover based on the older (1.0) and younger (0.0) parent.", new DoubleValue(1.0))
{
Hidden = true
});
Parameters.Add(new ValueParameter <IntArray>("AgeLimits", "The maximum age an individual is allowed to reach in a certain layer.", new IntArray(new int[0]))
{
Hidden = true
});
Parameters.Add(new ValueParameter <IntValue>("MatingPoolRange", "The range of layers used for creating a mating pool. (1 = current + previous layer)", new IntValue(1))
{
Hidden = true
});
Parameters.Add(new ValueParameter <BoolValue>("ReduceToPopulationSize", "Reduce the CurrentPopulationSize after elder migration to PopulationSize", new BoolValue(true))
{
Hidden = true
});
Parameters.Add(new ValueParameter <MultiTerminator>("Terminator", "The termination criteria that defines if the algorithm should continue or stop.", new MultiTerminator()));
#endregion
#region Create operators
var globalRandomCreator = new RandomCreator();
var layer0Creator = new SubScopesCreator()
{
Name = "Create Layer Zero"
};
var layer0Processor = new SubScopesProcessor();
var localRandomCreator = new LocalRandomCreator();
var layerSolutionsCreator = new SolutionsCreator();
var initializeAgeProcessor = new UniformSubScopesProcessor();
var initializeAge = new VariableCreator()
{
Name = "Initialize Age"
};
var initializeCurrentPopulationSize = new SubScopesCounter()
{
Name = "Initialize CurrentPopulationCounter"
};
var initializeLocalEvaluatedSolutions = new Assigner()
{
Name = "Initialize LayerEvaluatedSolutions"
};
var initializeGlobalEvaluatedSolutions = new DataReducer()
{
Name = "Initialize EvaluatedSolutions"
};
var resultsCollector = new ResultsCollector();
var mainLoop = new AlpsGeneticAlgorithmMainLoop();
#endregion
#region Create and parameterize operator graph
OperatorGraph.InitialOperator = globalRandomCreator;
globalRandomCreator.RandomParameter.ActualName = "GlobalRandom";
globalRandomCreator.SeedParameter.Value = null;
globalRandomCreator.SeedParameter.ActualName = SeedParameter.Name;
globalRandomCreator.SetSeedRandomlyParameter.Value = null;
globalRandomCreator.SetSeedRandomlyParameter.ActualName = SetSeedRandomlyParameter.Name;
globalRandomCreator.Successor = layer0Creator;
layer0Creator.NumberOfSubScopesParameter.Value = new IntValue(1);
layer0Creator.Successor = layer0Processor;
layer0Processor.Operators.Add(localRandomCreator);
layer0Processor.Successor = initializeGlobalEvaluatedSolutions;
localRandomCreator.Successor = layerSolutionsCreator;
layerSolutionsCreator.NumberOfSolutionsParameter.ActualName = PopulationSizeParameter.Name;
layerSolutionsCreator.Successor = initializeAgeProcessor;
initializeAgeProcessor.Operator = initializeAge;
initializeAgeProcessor.Successor = initializeCurrentPopulationSize;
initializeCurrentPopulationSize.ValueParameter.ActualName = "CurrentPopulationSize";
initializeCurrentPopulationSize.Successor = initializeLocalEvaluatedSolutions;
initializeAge.CollectedValues.Add(new ValueParameter <DoubleValue>("Age", new DoubleValue(0)));
initializeAge.Successor = null;
initializeLocalEvaluatedSolutions.LeftSideParameter.ActualName = "LayerEvaluatedSolutions";
initializeLocalEvaluatedSolutions.RightSideParameter.ActualName = "CurrentPopulationSize";
initializeLocalEvaluatedSolutions.Successor = null;
initializeGlobalEvaluatedSolutions.ReductionOperation.Value.Value = ReductionOperations.Sum;
initializeGlobalEvaluatedSolutions.TargetOperation.Value.Value = ReductionOperations.Assign;
initializeGlobalEvaluatedSolutions.ParameterToReduce.ActualName = "LayerEvaluatedSolutions";
initializeGlobalEvaluatedSolutions.TargetParameter.ActualName = "EvaluatedSolutions";
initializeGlobalEvaluatedSolutions.Successor = resultsCollector;
resultsCollector.CollectedValues.Add(new LookupParameter <IntValue>("Evaluated Solutions", null, "EvaluatedSolutions"));
resultsCollector.Successor = mainLoop;
mainLoop.GlobalRandomParameter.ActualName = "GlobalRandom";
mainLoop.LocalRandomParameter.ActualName = localRandomCreator.LocalRandomParameter.Name;
mainLoop.EvaluatedSolutionsParameter.ActualName = "EvaluatedSolutions";
mainLoop.AnalyzerParameter.ActualName = AnalyzerParameter.Name;
mainLoop.LayerAnalyzerParameter.ActualName = LayerAnalyzerParameter.Name;
mainLoop.NumberOfLayersParameter.ActualName = NumberOfLayersParameter.Name;
mainLoop.PopulationSizeParameter.ActualName = PopulationSizeParameter.Name;
mainLoop.CurrentPopulationSizeParameter.ActualName = "CurrentPopulationSize";
mainLoop.SelectorParameter.ActualName = SelectorParameter.Name;
mainLoop.CrossoverParameter.ActualName = CrossoverParameter.Name;
mainLoop.MutatorParameter.ActualName = MutatorParameter.Name;
mainLoop.MutationProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
mainLoop.ElitesParameter.ActualName = ElitesParameter.Name;
mainLoop.ReevaluateElitesParameter.ActualName = ReevaluateElitesParameter.Name;
mainLoop.PlusSelectionParameter.ActualName = PlusSelectionParameter.Name;
mainLoop.AgeParameter.ActualName = "Age";
mainLoop.AgeGapParameter.ActualName = AgeGapParameter.Name;
mainLoop.AgeInheritanceParameter.ActualName = AgeInheritanceParameter.Name;
mainLoop.AgeLimitsParameter.ActualName = AgeLimitsParameter.Name;
mainLoop.MatingPoolRangeParameter.ActualName = MatingPoolRangeParameter.Name;
mainLoop.ReduceToPopulationSizeParameter.ActualName = ReduceToPopulationSizeParameter.Name;
mainLoop.TerminatorParameter.ActualName = TerminatorParameter.Name;
#endregion
#region Set selectors
foreach (var selector in ApplicationManager.Manager.GetInstances <ISelector>().Where(s => !(s is IMultiObjectiveSelector)).OrderBy(s => Name))
{
SelectorParameter.ValidValues.Add(selector);
}
var defaultSelector = SelectorParameter.ValidValues.OfType <GeneralizedRankSelector>().FirstOrDefault();
if (defaultSelector != null)
{
defaultSelector.PressureParameter.Value = new DoubleValue(4.0);
SelectorParameter.Value = defaultSelector;
}
#endregion
#region Create analyzers
qualityAnalyzer = new BestAverageWorstQualityAnalyzer();
layerQualityAnalyzer = new BestAverageWorstQualityAnalyzer();
ageAnalyzer = new OldestAverageYoungestAgeAnalyzer();
layerAgeAnalyzer = new OldestAverageYoungestAgeAnalyzer();
ageDistributionAnalyzer = new AgeDistributionAnalyzer();
layerAgeDistributionAnalyzer = new AgeDistributionAnalyzer();
#endregion
#region Create terminators
generationsTerminator = new ComparisonTerminator <IntValue>("Generations", ComparisonType.Less, new IntValue(1000))
{
Name = "Generations"
};
evaluationsTerminator = new ComparisonTerminator <IntValue>("EvaluatedSolutions", ComparisonType.Less, new IntValue(int.MaxValue))
{
Name = "Evaluations"
};
qualityTerminator = new SingleObjectiveQualityTerminator()
{
Name = "Quality"
};
executionTimeTerminator = new ExecutionTimeTerminator(this, new TimeSpanValue(TimeSpan.FromMinutes(5)));
#endregion
#region Parameterize
UpdateAnalyzers();
ParameterizeAnalyzers();
ParameterizeSelectors();
UpdateTerminators();
ParameterizeAgeLimits();
#endregion
Initialize();
}
public SASEGASA()
: base()
{
Parameters.Add(new ValueParameter <IntValue>("Seed", "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
Parameters.Add(new ValueParameter <BoolValue>("SetSeedRandomly", "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
Parameters.Add(new ValueParameter <IntValue>("NumberOfVillages", "The initial number of villages.", new IntValue(10)));
Parameters.Add(new ValueParameter <IntValue>("PopulationSize", "The size of the population of solutions.", new IntValue(100)));
Parameters.Add(new ValueParameter <IntValue>("MaximumGenerations", "The maximum number of generations that should be processed.", new IntValue(1000)));
Parameters.Add(new ConstrainedValueParameter <ISelector>("Selector", "The operator used to select solutions for reproduction."));
Parameters.Add(new ConstrainedValueParameter <ICrossover>("Crossover", "The operator used to cross solutions."));
Parameters.Add(new ValueParameter <PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution.", new PercentValue(0.05)));
Parameters.Add(new OptionalConstrainedValueParameter <IManipulator>("Mutator", "The operator used to mutate solutions."));
Parameters.Add(new ValueParameter <IntValue>("Elites", "The numer of elite solutions which are kept in each generation.", new IntValue(1)));
Parameters.Add(new FixedValueParameter <BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)", new BoolValue(false))
{
Hidden = true
});
Parameters.Add(new ValueLookupParameter <DoubleValue>("SuccessRatio", "The ratio of successful to total children that should be achieved.", new DoubleValue(1)));
Parameters.Add(new ValueLookupParameter <DoubleValue>("ComparisonFactorLowerBound", "The lower bound of the comparison factor (start).", new DoubleValue(0.3)));
Parameters.Add(new ValueLookupParameter <DoubleValue>("ComparisonFactorUpperBound", "The upper bound of the comparison factor (end).", new DoubleValue(0.7)));
Parameters.Add(new OptionalConstrainedValueParameter <IDiscreteDoubleValueModifier>("ComparisonFactorModifier", "The operator used to modify the comparison factor.", new ItemSet <IDiscreteDoubleValueModifier>(new IDiscreteDoubleValueModifier[] { new LinearDiscreteDoubleValueModifier() }), new LinearDiscreteDoubleValueModifier()));
Parameters.Add(new ValueLookupParameter <DoubleValue>("MaximumSelectionPressure", "The maximum selection pressure that terminates the algorithm.", new DoubleValue(100)));
Parameters.Add(new ValueLookupParameter <DoubleValue>("FinalMaximumSelectionPressure", "The maximum selection pressure used when there is only one village left.", new DoubleValue(100)));
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.", new BoolValue(false)));
Parameters.Add(new ValueLookupParameter <IntValue>("SelectedParents", "How much parents should be selected each time the offspring selection step is performed until the population is filled. This parameter should be about the same or twice the size of PopulationSize for smaller problems, and less for large problems.", new IntValue(200)));
Parameters.Add(new ValueParameter <MultiAnalyzer>("Analyzer", "The operator used to analyze the villages.", new MultiAnalyzer()));
Parameters.Add(new ValueParameter <MultiAnalyzer>("VillageAnalyzer", "The operator used to analyze each village.", new MultiAnalyzer()));
Parameters.Add(new ValueParameter <IntValue>("MaximumEvaluatedSolutions", "The maximum number of evaluated solutions (approximately).", new IntValue(int.MaxValue)));
Parameters.Add(new FixedValueParameter <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.", new BoolValue(true))
{
Hidden = true
});
RandomCreator randomCreator = new RandomCreator();
SubScopesCreator populationCreator = new SubScopesCreator();
UniformSubScopesProcessor ussp1 = new UniformSubScopesProcessor();
SolutionsCreator solutionsCreator = new SolutionsCreator();
VariableCreator variableCreator = new VariableCreator();
UniformSubScopesProcessor ussp2 = new UniformSubScopesProcessor();
SubScopesCounter subScopesCounter = new SubScopesCounter();
ResultsCollector resultsCollector = new ResultsCollector();
SASEGASAMainLoop mainLoop = new SASEGASAMainLoop();
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 = populationCreator;
populationCreator.NumberOfSubScopesParameter.ActualName = NumberOfVillagesParameter.Name;
populationCreator.Successor = ussp1;
ussp1.Operator = solutionsCreator;
ussp1.Successor = variableCreator;
solutionsCreator.NumberOfSolutionsParameter.ActualName = PopulationSizeParameter.Name;
solutionsCreator.Successor = null;
variableCreator.Name = "Initialize EvaluatedSolutions";
variableCreator.CollectedValues.Add(new ValueParameter <IntValue>("EvaluatedSolutions", new IntValue()));
variableCreator.Successor = ussp2;
ussp2.Operator = subScopesCounter;
ussp2.Successor = resultsCollector;
subScopesCounter.Name = "Increment EvaluatedSolutions";
subScopesCounter.ValueParameter.ActualName = "EvaluatedSolutions";
resultsCollector.CollectedValues.Add(new LookupParameter <IntValue>("Evaluated Solutions", "", "EvaluatedSolutions"));
resultsCollector.ResultsParameter.ActualName = "Results";
resultsCollector.Successor = mainLoop;
mainLoop.NumberOfVillagesParameter.ActualName = NumberOfVillagesParameter.Name;
mainLoop.SelectorParameter.ActualName = SelectorParameter.Name;
mainLoop.CrossoverParameter.ActualName = CrossoverParameter.Name;
mainLoop.ElitesParameter.ActualName = ElitesParameter.Name;
mainLoop.ReevaluateElitesParameter.ActualName = ReevaluateElitesParameter.Name;
mainLoop.MutatorParameter.ActualName = MutatorParameter.Name;
mainLoop.MutationProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
mainLoop.RandomParameter.ActualName = randomCreator.RandomParameter.ActualName;
mainLoop.ResultsParameter.ActualName = "Results";
mainLoop.SuccessRatioParameter.ActualName = SuccessRatioParameter.Name;
mainLoop.ComparisonFactorStartParameter.ActualName = ComparisonFactorLowerBoundParameter.Name;
mainLoop.ComparisonFactorModifierParameter.ActualName = ComparisonFactorModifierParameter.Name;
mainLoop.MaximumSelectionPressureParameter.ActualName = MaximumSelectionPressureParameter.Name;
mainLoop.FinalMaximumSelectionPressureParameter.ActualName = FinalMaximumSelectionPressureParameter.Name;
mainLoop.MaximumGenerationsParameter.ActualName = MaximumGenerationsParameter.Name;
mainLoop.OffspringSelectionBeforeMutationParameter.ActualName = OffspringSelectionBeforeMutationParameter.Name;
mainLoop.EvaluatedSolutionsParameter.ActualName = "EvaluatedSolutions";
mainLoop.FillPopulationWithParentsParameter.ActualName = FillPopulationWithParentsParameter.Name;
mainLoop.Successor = null;
foreach (ISelector selector in ApplicationManager.Manager.GetInstances <ISelector>().Where(x => !(x is IMultiObjectiveSelector)).OrderBy(x => x.Name))
{
SelectorParameter.ValidValues.Add(selector);
}
ISelector proportionalSelector = SelectorParameter.ValidValues.FirstOrDefault(x => x.GetType().Name.Equals("ProportionalSelector"));
if (proportionalSelector != null)
{
SelectorParameter.Value = proportionalSelector;
}
ParameterizeSelectors();
foreach (IDiscreteDoubleValueModifier modifier in ApplicationManager.Manager.GetInstances <IDiscreteDoubleValueModifier>().OrderBy(x => x.Name))
{
ComparisonFactorModifierParameter.ValidValues.Add(modifier);
}
IDiscreteDoubleValueModifier linearModifier = ComparisonFactorModifierParameter.ValidValues.FirstOrDefault(x => x.GetType().Name.Equals("LinearDiscreteDoubleValueModifier"));
if (linearModifier != null)
{
ComparisonFactorModifierParameter.Value = linearModifier;
}
ParameterizeComparisonFactorModifiers();
qualityAnalyzer = new BestAverageWorstQualityAnalyzer();
villageQualityAnalyzer = new BestAverageWorstQualityAnalyzer();
selectionPressureAnalyzer = new ValueAnalyzer();
villageSelectionPressureAnalyzer = new ValueAnalyzer();
successfulOffspringAnalyzer = new SuccessfulOffspringAnalyzer();
ParameterizeAnalyzers();
UpdateAnalyzers();
Initialize();
}
public RobustTabooSearch()
{
Parameters.Add(new ValueParameter <MultiAnalyzer>("Analyzer", "The analyzers that are applied after each iteration.", new MultiAnalyzer()));
Parameters.Add(new FixedValueParameter <IntValue>("Seed", "The seed value of the random number generator.", new IntValue(0)));
Parameters.Add(new FixedValueParameter <BoolValue>("SetSeedRandomly", "True whether the seed should be set randomly for each run, false if it should be fixed.", new BoolValue(true)));
Parameters.Add(new FixedValueParameter <IntValue>("MaximumIterations", "The number of iterations that the algorithm should run.", new IntValue(10000)));
Parameters.Add(new FixedValueParameter <IntValue>("MinimumTabuTenure", "The minimum tabu tenure.", new IntValue(10)));
Parameters.Add(new FixedValueParameter <IntValue>("MaximumTabuTenure", "The maximum tabu tenure.", new IntValue(20)));
Parameters.Add(new FixedValueParameter <BoolValue>("UseAlternativeAspiration", "True if the alternative aspiration condition should be used that takes moves that have not been made for some time above others.", new BoolValue(false)));
Parameters.Add(new FixedValueParameter <IntValue>("AlternativeAspirationTenure", "The time t that a move will be remembered for the alternative aspiration condition.", new IntValue(int.MaxValue)));
Parameters.Add(new FixedValueParameter <BoolValue>("TerminateOnOptimalSolution", "True when the algorithm should stop if it reached a quality equal or smaller to the BestKnownQuality.", new BoolValue(false)));
Parameters.Add(new FixedValueParameter <BoolValue>("UseNewTabuTenureAdaptionScheme", @"In an updated version of his implementation, Eric Taillard introduced a different way to change the tabu tenure.
Instead of setting it uniformly between min and max, it will be set between 0 and max according to a right-skewed distribution.
Set this option to false if you want to optimize using the earlier 1991 version, and set to true if you want to optimize using the newer version.
Please note that the MinimumTabuTenure parameter has no effect in the new version.", new BoolValue(true)));
TerminateOnOptimalSolutionParameter.Hidden = true;
qualityAnalyzer = new BestAverageWorstQualityAnalyzer();
qualityAnalyzer.ResultsParameter.ActualName = "Results";
AnalyzerParameter.Value.Operators.Add(qualityAnalyzer);
RandomCreator randomCreator = new RandomCreator();
randomCreator.RandomParameter.ActualName = "Random";
randomCreator.SeedParameter.Value = null;
randomCreator.SeedParameter.ActualName = SeedParameter.Name;
randomCreator.SetSeedRandomlyParameter.Value = null;
randomCreator.SetSeedRandomlyParameter.ActualName = SetSeedRandomlyParameter.Name;
VariableCreator variableCreator = new VariableCreator();
variableCreator.CollectedValues.Add(new ValueParameter <IntValue>("Iterations", new IntValue(0)));
variableCreator.CollectedValues.Add(new ValueParameter <IntValue>("EvaluatedSolutions", new IntValue(1)));
variableCreator.CollectedValues.Add(new ValueParameter <DoubleValue>("EvaluatedSolutionEquivalents", new DoubleValue(1)));
ResultsCollector resultsCollector = new ResultsCollector();
resultsCollector.CollectedValues.Add(new LookupParameter <IntValue>("Iterations", "The actual iteration."));
resultsCollector.CollectedValues.Add(new LookupParameter <IntValue>("EvaluatedSolutions", "Number of evaluated solutions."));
solutionsCreator = new SolutionsCreator();
solutionsCreator.NumberOfSolutions = new IntValue(1);
Placeholder analyzer = new Placeholder();
analyzer.Name = "(Analyzer)";
analyzer.OperatorParameter.ActualName = AnalyzerParameter.Name;
UniformSubScopesProcessor ussp = new UniformSubScopesProcessor();
mainOperator = new RobustTabooSeachOperator();
mainOperator.AlternativeAspirationTenureParameter.ActualName = AlternativeAspirationTenureParameter.Name;
mainOperator.BestQualityParameter.ActualName = "BestSoFarQuality";
mainOperator.IterationsParameter.ActualName = "Iterations";
mainOperator.LastMoveParameter.ActualName = "LastMove";
mainOperator.MaximumIterationsParameter.ActualName = MaximumIterationsParameter.Name;
mainOperator.MaximumTabuTenureParameter.ActualName = MaximumTabuTenureParameter.Name;
mainOperator.MinimumTabuTenureParameter.ActualName = MinimumTabuTenureParameter.Name;
mainOperator.MoveQualityMatrixParameter.ActualName = "MoveQualityMatrix";
mainOperator.RandomParameter.ActualName = "Random";
mainOperator.ResultsParameter.ActualName = "Results";
mainOperator.ShortTermMemoryParameter.ActualName = "ShortTermMemory";
mainOperator.UseAlternativeAspirationParameter.ActualName = UseAlternativeAspirationParameter.Name;
mainOperator.EvaluatedSolutionsParameter.ActualName = "EvaluatedSolutions";
mainOperator.EvaluatedSolutionEquivalentsParameter.ActualName = "EvaluatedSolutionEquivalents";
ConditionalBranch qualityStopBranch = new ConditionalBranch();
qualityStopBranch.Name = "Terminate on optimal quality?";
qualityStopBranch.ConditionParameter.ActualName = "TerminateOnOptimalSolution";
Comparator qualityComparator = new Comparator();
qualityComparator.Comparison = new Comparison(ComparisonType.Greater);
qualityComparator.LeftSideParameter.ActualName = "BestQuality";
qualityComparator.RightSideParameter.ActualName = "BestKnownQuality";
qualityComparator.ResultParameter.ActualName = "ContinueByQuality";
ConditionalBranch continueByQualityBranch = new ConditionalBranch();
continueByQualityBranch.ConditionParameter.ActualName = "ContinueByQuality";
IntCounter iterationsCounter = new IntCounter();
iterationsCounter.ValueParameter.ActualName = "Iterations";
iterationsCounter.Increment = new IntValue(1);
Comparator comparator = new Comparator();
comparator.Name = "Iterations < MaximumIterations ?";
comparator.LeftSideParameter.ActualName = "Iterations";
comparator.RightSideParameter.ActualName = MaximumIterationsParameter.Name;
comparator.Comparison = new Comparison(ComparisonType.Less);
comparator.ResultParameter.ActualName = "ContinueByIteration";
ConditionalBranch continueByIterationBranch = new ConditionalBranch();
continueByIterationBranch.ConditionParameter.ActualName = "ContinueByIteration";
OperatorGraph.InitialOperator = randomCreator;
randomCreator.Successor = variableCreator;
variableCreator.Successor = resultsCollector;
resultsCollector.Successor = solutionsCreator;
solutionsCreator.Successor = analyzer;
analyzer.Successor = ussp;
ussp.Operator = mainOperator;
ussp.Successor = qualityStopBranch;
qualityStopBranch.FalseBranch = iterationsCounter;
qualityStopBranch.TrueBranch = qualityComparator;
qualityStopBranch.Successor = null;
qualityComparator.Successor = continueByQualityBranch;
continueByQualityBranch.TrueBranch = iterationsCounter;
continueByQualityBranch.FalseBranch = null;
continueByQualityBranch.Successor = null;
iterationsCounter.Successor = comparator;
comparator.Successor = continueByIterationBranch;
continueByIterationBranch.TrueBranch = analyzer;
continueByIterationBranch.FalseBranch = null;
continueByIterationBranch.Successor = null;
RegisterEventHandlers();
Problem = new QuadraticAssignmentProblem();
}
public EvolutionStrategy()
: base()
{
Parameters.Add(new ValueParameter <IntValue>("Seed", "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
Parameters.Add(new ValueParameter <BoolValue>("SetSeedRandomly", "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
Parameters.Add(new ValueParameter <IntValue>("PopulationSize", "µ (mu) - the size of the population.", new IntValue(20)));
Parameters.Add(new ValueParameter <IntValue>("ParentsPerChild", "ρ (rho) - how many parents should be recombined.", new IntValue(1)));
Parameters.Add(new ValueParameter <IntValue>("Children", "λ (lambda) - the size of the offspring population.", new IntValue(100)));
Parameters.Add(new ValueParameter <IntValue>("MaximumGenerations", "The maximum number of generations which should be processed.", new IntValue(1000)));
Parameters.Add(new ValueParameter <BoolValue>("PlusSelection", "True for plus selection (elitist population), false for comma selection (non-elitist population).", new BoolValue(true)));
Parameters.Add(new FixedValueParameter <BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)", new BoolValue(false))
{
Hidden = true
});
Parameters.Add(new OptionalConstrainedValueParameter <ICrossover>("Recombinator", "The operator used to cross solutions."));
Parameters.Add(new ConstrainedValueParameter <IManipulator>("Mutator", "The operator used to mutate solutions."));
Parameters.Add(new OptionalConstrainedValueParameter <IStrategyParameterCreator>("StrategyParameterCreator", "The operator that creates the strategy parameters."));
Parameters.Add(new OptionalConstrainedValueParameter <IStrategyParameterCrossover>("StrategyParameterCrossover", "The operator that recombines the strategy parameters."));
Parameters.Add(new OptionalConstrainedValueParameter <IStrategyParameterManipulator>("StrategyParameterManipulator", "The operator that manipulates the strategy parameters."));
Parameters.Add(new ValueParameter <MultiAnalyzer>("Analyzer", "The operator used to analyze each generation.", new MultiAnalyzer()));
RandomCreator randomCreator = new RandomCreator();
SolutionsCreator solutionsCreator = new SolutionsCreator();
SubScopesCounter subScopesCounter = new SubScopesCounter();
UniformSubScopesProcessor strategyVectorProcessor = new UniformSubScopesProcessor();
Placeholder strategyVectorCreator = new Placeholder();
ResultsCollector resultsCollector = new ResultsCollector();
EvolutionStrategyMainLoop mainLoop = new EvolutionStrategyMainLoop();
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 = solutionsCreator;
solutionsCreator.NumberOfSolutionsParameter.ActualName = PopulationSizeParameter.Name;
solutionsCreator.Successor = subScopesCounter;
subScopesCounter.Name = "Initialize EvaluatedSolutions";
subScopesCounter.ValueParameter.ActualName = "EvaluatedSolutions";
subScopesCounter.Successor = strategyVectorProcessor;
strategyVectorProcessor.Operator = strategyVectorCreator;
strategyVectorProcessor.Successor = resultsCollector;
strategyVectorCreator.OperatorParameter.ActualName = "StrategyParameterCreator";
resultsCollector.CollectedValues.Add(new LookupParameter <IntValue>("Evaluated Solutions", null, "EvaluatedSolutions"));
resultsCollector.ResultsParameter.ActualName = "Results";
resultsCollector.Successor = mainLoop;
mainLoop.RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
mainLoop.PopulationSizeParameter.ActualName = PopulationSizeParameter.Name;
mainLoop.ParentsPerChildParameter.ActualName = ParentsPerChildParameter.Name;
mainLoop.ChildrenParameter.ActualName = ChildrenParameter.Name;
mainLoop.MaximumGenerationsParameter.ActualName = MaximumGenerationsParameter.Name;
mainLoop.PlusSelectionParameter.ActualName = PlusSelectionParameter.Name;
mainLoop.ReevaluateElitesParameter.ActualName = ReevaluateElitesParameter.Name;
mainLoop.MutatorParameter.ActualName = MutatorParameter.Name;
mainLoop.RecombinatorParameter.ActualName = RecombinatorParameter.Name;
mainLoop.AnalyzerParameter.ActualName = AnalyzerParameter.Name;
mainLoop.ResultsParameter.ActualName = "Results";
mainLoop.EvaluatedSolutionsParameter.ActualName = "EvaluatedSolutions";
qualityAnalyzer = new BestAverageWorstQualityAnalyzer();
ParameterizeAnalyzers();
UpdateAnalyzers();
Initialize();
}
public OffspringSelectionEvolutionStrategy()
: base()
{
Parameters.Add(new ValueParameter <IntValue>("Seed", "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
Parameters.Add(new ValueParameter <BoolValue>("SetSeedRandomly", "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
Parameters.Add(new ValueParameter <IntValue>("PopulationSize", "µ (mu) - the size of the population.", new IntValue(20)));
Parameters.Add(new ValueParameter <IntValue>("ParentsPerChild", "ρ (rho) - how many parents should be recombined.", new IntValue(1)));
Parameters.Add(new ValueParameter <IntValue>("MaximumGenerations", "The maximum number of generations which should be processed.", new IntValue(1000)));
Parameters.Add(new ValueParameter <BoolValue>("PlusSelection", "True for plus selection (elitist population), false for comma selection (non-elitist population).", new BoolValue(true)));
Parameters.Add(new FixedValueParameter <BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)", new BoolValue(false))
{
Hidden = true
});
Parameters.Add(new OptionalConstrainedValueParameter <ICrossover>("Recombinator", "The operator used to cross solutions."));
Parameters.Add(new ConstrainedValueParameter <IManipulator>("Mutator", "The operator used to mutate solutions."));
Parameters.Add(new OptionalConstrainedValueParameter <IStrategyParameterCreator>("StrategyParameterCreator", "The operator that creates the strategy parameters."));
Parameters.Add(new OptionalConstrainedValueParameter <IStrategyParameterCrossover>("StrategyParameterCrossover", "The operator that recombines the strategy parameters."));
Parameters.Add(new OptionalConstrainedValueParameter <IStrategyParameterManipulator>("StrategyParameterManipulator", "The operator that manipulates the strategy parameters."));
Parameters.Add(new ValueParameter <MultiAnalyzer>("Analyzer", "The operator used to analyze each generation.", new MultiAnalyzer()));
Parameters.Add(new ValueLookupParameter <DoubleValue>("SuccessRatio", "The ratio of successful to total children that should be achieved.", new DoubleValue(1)));
Parameters.Add(new ValueLookupParameter <IntValue>("SelectedParents", "How much parents should be selected each time the offspring selection step is performed until the population is filled. This parameter should be about the same or twice the size of PopulationSize for smaller problems, and less for large problems.", new IntValue(40)));
Parameters.Add(new ValueLookupParameter <DoubleValue>("MaximumSelectionPressure", "The maximum selection pressure that terminates the algorithm.", new DoubleValue(100)));
Parameters.Add(new ValueParameter <IntValue>("MaximumEvaluatedSolutions", "The maximum number of evaluated solutions.", new IntValue(int.MaxValue)));
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].", new DoubleValue(0.5)));
RandomCreator randomCreator = new RandomCreator();
SolutionsCreator solutionsCreator = new SolutionsCreator();
SubScopesCounter subScopesCounter = new SubScopesCounter();
UniformSubScopesProcessor strategyVectorProcessor = new UniformSubScopesProcessor();
Placeholder strategyVectorCreator = new Placeholder();
ResultsCollector resultsCollector = new ResultsCollector();
OffspringSelectionEvolutionStrategyMainLoop mainLoop = new OffspringSelectionEvolutionStrategyMainLoop();
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 = solutionsCreator;
solutionsCreator.NumberOfSolutionsParameter.ActualName = PopulationSizeParameter.Name;
solutionsCreator.Successor = subScopesCounter;
subScopesCounter.Name = "Initialize EvaluatedSolutions";
subScopesCounter.ValueParameter.ActualName = "EvaluatedSolutions";
subScopesCounter.Successor = strategyVectorProcessor;
strategyVectorProcessor.Operator = strategyVectorCreator;
strategyVectorProcessor.Successor = resultsCollector;
strategyVectorCreator.OperatorParameter.ActualName = "StrategyParameterCreator";
resultsCollector.CollectedValues.Add(new LookupParameter <IntValue>("Evaluated Solutions", null, "EvaluatedSolutions"));
resultsCollector.ResultsParameter.ActualName = "Results";
resultsCollector.Successor = mainLoop;
mainLoop.RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
mainLoop.PopulationSizeParameter.ActualName = PopulationSizeParameter.Name;
mainLoop.ParentsPerChildParameter.ActualName = ParentsPerChildParameter.Name;
mainLoop.MaximumGenerationsParameter.ActualName = MaximumGenerationsParameter.Name;
mainLoop.PlusSelectionParameter.ActualName = PlusSelectionParameter.Name;
mainLoop.ReevaluateElitesParameter.ActualName = ReevaluateElitesParameter.Name;
mainLoop.MutatorParameter.ActualName = MutatorParameter.Name;
mainLoop.RecombinatorParameter.ActualName = RecombinatorParameter.Name;
mainLoop.AnalyzerParameter.ActualName = AnalyzerParameter.Name;
mainLoop.ResultsParameter.ActualName = "Results";
mainLoop.EvaluatedSolutionsParameter.ActualName = "EvaluatedSolutions";
mainLoop.SuccessRatioParameter.ActualName = SuccessRatioParameter.Name;
mainLoop.MaximumSelectionPressureParameter.ActualName = MaximumSelectionPressureParameter.Name;
mainLoop.MaximumEvaluatedSolutionsParameter.ActualName = MaximumEvaluatedSolutionsParameter.Name;
mainLoop.SelectedParentsParameter.ActualName = SelectedParentsParameter.Name;
mainLoop.ComparisonFactorParameter.ActualName = ComparisonFactorParameter.Name;
mainLoop.CurrentSuccessRatioParameter.ActualName = "CurrentSuccessRatio";
mainLoop.SelectionPressureParameter.ActualName = "SelectionPressure";
qualityAnalyzer = new BestAverageWorstQualityAnalyzer();
selectionPressureAnalyzer = new ValueAnalyzer();
ParameterizeAnalyzers();
UpdateAnalyzers();
Initialize();
}
public ParticleSwarmOptimization()
: base()
{
Parameters.Add(new ValueParameter <IntValue>("Seed", "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
Parameters.Add(new ValueParameter <BoolValue>("SetSeedRandomly", "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
Parameters.Add(new ValueParameter <IntValue>("SwarmSize", "Size of the particle swarm.", new IntValue(10)));
Parameters.Add(new ValueParameter <IntValue>("MaxIterations", "Maximal number of iterations.", new IntValue(1000)));
Parameters.Add(new ValueParameter <MultiAnalyzer>("Analyzer", "The operator used to analyze each generation.", new MultiAnalyzer()));
Parameters.Add(new ValueParameter <DoubleValue>("Inertia", "Inertia weight on a particle's movement (omega).", new DoubleValue(1)));
Parameters.Add(new ValueParameter <DoubleValue>("PersonalBestAttraction", "Weight for particle's pull towards its personal best soution (phi_p).", new DoubleValue(-0.01)));
Parameters.Add(new ValueParameter <DoubleValue>("NeighborBestAttraction", "Weight for pull towards the neighborhood best solution or global best solution in case of a totally connected topology (phi_g).", new DoubleValue(3.7)));
Parameters.Add(new ConstrainedValueParameter <IParticleCreator>("ParticleCreator", "Operator that creates a new particle."));
Parameters.Add(new ConstrainedValueParameter <IParticleUpdater>("ParticleUpdater", "Operator that updates a particle."));
Parameters.Add(new OptionalConstrainedValueParameter <ITopologyInitializer>("TopologyInitializer", "Creates neighborhood description vectors."));
Parameters.Add(new OptionalConstrainedValueParameter <ITopologyUpdater>("TopologyUpdater", "Updates the neighborhood description vectors."));
Parameters.Add(new OptionalConstrainedValueParameter <IDiscreteDoubleValueModifier>("InertiaUpdater", "Updates the omega parameter."));
Parameters.Add(new ConstrainedValueParameter <ISwarmUpdater>("SwarmUpdater", "Encoding-specific parameter which is provided by the problem. May provide additional encoding-specific parameters, such as velocity bounds for real valued problems"));
ParticleUpdaterParameter.Hidden = true;
RandomCreator randomCreator = new RandomCreator();
VariableCreator variableCreator = new VariableCreator();
Assigner currentInertiaAssigner = new Assigner();
solutionsCreator = new SolutionsCreator();
SubScopesCounter subScopesCounter = new SubScopesCounter();
Placeholder topologyInitializerPlaceholder = new Placeholder();
mainLoop = new ParticleSwarmOptimizationMainLoop();
OperatorGraph.InitialOperator = randomCreator;
randomCreator.SetSeedRandomlyParameter.Value = null;
randomCreator.SeedParameter.Value = null;
randomCreator.Successor = variableCreator;
variableCreator.CollectedValues.Add(new ValueParameter <IntValue>("Iterations", new IntValue(0)));
variableCreator.Successor = currentInertiaAssigner;
currentInertiaAssigner.Name = "CurrentInertia := Inertia";
currentInertiaAssigner.LeftSideParameter.ActualName = "CurrentInertia";
currentInertiaAssigner.RightSideParameter.ActualName = "Inertia";
currentInertiaAssigner.Successor = solutionsCreator;
solutionsCreator.NumberOfSolutionsParameter.ActualName = "SwarmSize";
ParameterizeSolutionsCreator();
solutionsCreator.Successor = subScopesCounter;
subScopesCounter.Name = "Initialize EvaluatedSolutions";
subScopesCounter.ValueParameter.ActualName = "EvaluatedSolutions";
subScopesCounter.Successor = topologyInitializerPlaceholder;
topologyInitializerPlaceholder.Name = "(TopologyInitializer)";
topologyInitializerPlaceholder.OperatorParameter.ActualName = "TopologyInitializer";
topologyInitializerPlaceholder.Successor = mainLoop;
mainLoop.AnalyzerParameter.ActualName = AnalyzerParameter.Name;
mainLoop.InertiaParameter.ActualName = "CurrentInertia";
mainLoop.MaxIterationsParameter.ActualName = MaxIterationsParameter.Name;
mainLoop.NeighborBestAttractionParameter.ActualName = NeighborBestAttractionParameter.Name;
mainLoop.InertiaUpdaterParameter.ActualName = InertiaUpdaterParameter.Name;
mainLoop.ParticleUpdaterParameter.ActualName = ParticleUpdaterParameter.Name;
mainLoop.PersonalBestAttractionParameter.ActualName = PersonalBestAttractionParameter.Name;
mainLoop.RandomParameter.ActualName = randomCreator.RandomParameter.ActualName;
mainLoop.SwarmSizeParameter.ActualName = SwarmSizeParameter.Name;
mainLoop.TopologyUpdaterParameter.ActualName = TopologyUpdaterParameter.Name;
mainLoop.RandomParameter.ActualName = randomCreator.RandomParameter.ActualName;
mainLoop.ResultsParameter.ActualName = "Results";
InitializeAnalyzers();
InitializeParticleCreator();
InitializeSwarmUpdater();
ParameterizeSolutionsCreator();
UpdateAnalyzers();
UpdateInertiaUpdater();
InitInertiaUpdater();
UpdateTopologyInitializer();
Initialize();
ParameterizeMainLoop();
}
public SimulatedAnnealing()
: base()
{
Parameters.Add(new ValueParameter <IntValue>("Seed", "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
Parameters.Add(new ValueParameter <BoolValue>("SetSeedRandomly", "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
Parameters.Add(new ConstrainedValueParameter <IMultiMoveGenerator>("MoveGenerator", "The operator used to generate moves to the neighborhood of the current solution."));
Parameters.Add(new ConstrainedValueParameter <ISingleObjectiveMoveEvaluator>("MoveEvaluator", "The operator used to evaluate a move."));
Parameters.Add(new ConstrainedValueParameter <IMoveMaker>("MoveMaker", "The operator used to perform a move."));
Parameters.Add(new ConstrainedValueParameter <IDiscreteDoubleValueModifier>("AnnealingOperator", "The operator used to modify the temperature."));
Parameters.Add(new ValueParameter <IntValue>("MaximumIterations", "The maximum number of generations which should be processed.", new IntValue(100)));
Parameters.Add(new ValueParameter <IntValue>("InnerIterations", "The amount of inner iterations (number of moves before temperature is adjusted again).", new IntValue(10)));
Parameters.Add(new ValueParameter <DoubleValue>("StartTemperature", "The initial temperature.", new DoubleValue(100)));
Parameters.Add(new ValueParameter <DoubleValue>("EndTemperature", "The final temperature which should be reached when iterations reaches maximum iterations.", new DoubleValue(1e-6)));
Parameters.Add(new ValueParameter <MultiAnalyzer>("Analyzer", "The operator used to analyze each generation.", new MultiAnalyzer()));
RandomCreator randomCreator = new RandomCreator();
SolutionsCreator solutionsCreator = new SolutionsCreator();
VariableCreator variableCreator = new VariableCreator();
ResultsCollector resultsCollector = new ResultsCollector();
SimulatedAnnealingMainLoop mainLoop = new SimulatedAnnealingMainLoop();
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 = solutionsCreator;
solutionsCreator.NumberOfSolutions = new IntValue(1);
solutionsCreator.Successor = variableCreator;
variableCreator.Name = "Initialize EvaluatedMoves";
variableCreator.CollectedValues.Add(new ValueParameter <IntValue>("EvaluatedMoves", new IntValue()));
variableCreator.CollectedValues.Add(new ValueParameter <IntValue>("Iterations", new IntValue(0)));
variableCreator.Successor = resultsCollector;
resultsCollector.CollectedValues.Add(new LookupParameter <IntValue>("Evaluated Moves", null, "EvaluatedMoves"));
resultsCollector.ResultsParameter.ActualName = "Results";
resultsCollector.Successor = mainLoop;
mainLoop.MoveGeneratorParameter.ActualName = MoveGeneratorParameter.Name;
mainLoop.MoveEvaluatorParameter.ActualName = MoveEvaluatorParameter.Name;
mainLoop.MoveMakerParameter.ActualName = MoveMakerParameter.Name;
mainLoop.AnnealingOperatorParameter.ActualName = AnnealingOperatorParameter.Name;
mainLoop.MaximumIterationsParameter.ActualName = MaximumIterationsParameter.Name;
mainLoop.TemperatureParameter.ActualName = "Temperature";
mainLoop.StartTemperatureParameter.ActualName = StartTemperatureParameter.Name;
mainLoop.EndTemperatureParameter.ActualName = EndTemperatureParameter.Name;
mainLoop.RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
mainLoop.ResultsParameter.ActualName = "Results";
mainLoop.AnalyzerParameter.ActualName = AnalyzerParameter.Name;
mainLoop.EvaluatedMovesParameter.ActualName = "EvaluatedMoves";
mainLoop.IterationsParameter.ActualName = "Iterations";
foreach (IDiscreteDoubleValueModifier op in ApplicationManager.Manager.GetInstances <IDiscreteDoubleValueModifier>().OrderBy(x => x.Name))
{
AnnealingOperatorParameter.ValidValues.Add(op);
}
ParameterizeAnnealingOperators();
qualityAnalyzer = new QualityAnalyzer();
temperatureAnalyzer = new SingleValueAnalyzer();
temperatureAnalyzer.Name = "TemperatureAnalyzer";
ParameterizeAnalyzers();
UpdateAnalyzers();
Initialize();
}