private ParticleSwarmOptimization(ParticleSwarmOptimization original, Cloner cloner)
: base(original, cloner) {
qualityAnalyzer = cloner.Clone(original.qualityAnalyzer);
solutionsCreator = cloner.Clone(original.solutionsCreator);
mainLoop = cloner.Clone(original.mainLoop);
Initialize();
}
public TabuSearch()
: 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<IMoveGenerator>("MoveGenerator", "The operator used to generate moves to the neighborhood of the current solution."));
Parameters.Add(new ConstrainedValueParameter<IMoveMaker>("MoveMaker", "The operator used to perform a move."));
Parameters.Add(new ConstrainedValueParameter<ISingleObjectiveMoveEvaluator>("MoveEvaluator", "The operator used to evaluate a move."));
Parameters.Add(new ConstrainedValueParameter<ITabuChecker>("TabuChecker", "The operator to check whether a move is tabu or not."));
Parameters.Add(new ConstrainedValueParameter<ITabuMaker>("TabuMaker", "The operator used to insert attributes of a move into the tabu list."));
Parameters.Add(new ValueParameter<IntValue>("TabuTenure", "The length of the tabu list.", new IntValue(10)));
Parameters.Add(new ValueParameter<IntValue>("MaximumIterations", "The maximum number of generations which should be processed.", new IntValue(1000)));
Parameters.Add(new ValueParameter<IntValue>("SampleSize", "The neighborhood size for stochastic sampling move generators", new IntValue(100)));
Parameters.Add(new ValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze the solution.", new MultiAnalyzer()));
RandomCreator randomCreator = new RandomCreator();
SolutionsCreator solutionsCreator = new SolutionsCreator();
VariableCreator variableCreator = new VariableCreator();
ResultsCollector resultsCollector = new ResultsCollector();
TabuSearchMainLoop mainLoop = new TabuSearchMainLoop();
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.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.MoveMakerParameter.ActualName = MoveMakerParameter.Name;
mainLoop.MoveEvaluatorParameter.ActualName = MoveEvaluatorParameter.Name;
mainLoop.TabuCheckerParameter.ActualName = TabuCheckerParameter.Name;
mainLoop.TabuMakerParameter.ActualName = TabuMakerParameter.Name;
mainLoop.MaximumIterationsParameter.ActualName = MaximumIterationsParameter.Name;
mainLoop.RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
mainLoop.ResultsParameter.ActualName = "Results";
mainLoop.AnalyzerParameter.ActualName = AnalyzerParameter.Name;
mainLoop.EvaluatedMovesParameter.ActualName = "EvaluatedMoves";
moveQualityAnalyzer = new BestAverageWorstQualityAnalyzer();
tabuNeighborhoodAnalyzer = new TabuNeighborhoodAnalyzer();
ParameterizeAnalyzers();
UpdateAnalyzers();
Initialize();
}
private RobustTabooSearch(RobustTabooSearch original, Cloner cloner)
: base(original, cloner) {
solutionsCreator = cloner.Clone(original.solutionsCreator);
mainOperator = cloner.Clone(original.mainOperator);
qualityAnalyzer = cloner.Clone(original.qualityAnalyzer);
RegisterEventHandlers();
}
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();
}
private OffspringSelectionGeneticAlgorithm(OffspringSelectionGeneticAlgorithm original, Cloner cloner)
: base(original, cloner) {
qualityAnalyzer = cloner.Clone(original.qualityAnalyzer);
selectionPressureAnalyzer = cloner.Clone(original.selectionPressureAnalyzer);
successfulOffspringAnalyzer = cloner.Clone(original.successfulOffspringAnalyzer);
Initialize();
}
private AlpsGeneticAlgorithm(AlpsGeneticAlgorithm original, Cloner cloner)
: base(original, cloner) {
qualityAnalyzer = cloner.Clone(original.qualityAnalyzer);
layerQualityAnalyzer = cloner.Clone(original.layerQualityAnalyzer);
ageAnalyzer = cloner.Clone(original.ageAnalyzer);
layerAgeAnalyzer = cloner.Clone(original.layerAgeAnalyzer);
ageDistributionAnalyzer = cloner.Clone(original.ageDistributionAnalyzer);
layerAgeDistributionAnalyzer = cloner.Clone(original.layerAgeDistributionAnalyzer);
generationsTerminator = cloner.Clone(original.generationsTerminator);
evaluationsTerminator = cloner.Clone(original.evaluationsTerminator);
qualityTerminator = cloner.Clone(original.qualityTerminator);
executionTimeTerminator = cloner.Clone(original.executionTimeTerminator);
Initialize();
}
private LocalSearchImprovementOperator(LocalSearchImprovementOperator original, Cloner cloner)
: base(original, cloner) {
this.loop = cloner.Clone(original.loop);
this.qualityAnalyzer = cloner.Clone(original.qualityAnalyzer);
this.problem = cloner.Clone(original.problem);
RegisterEventHandlers();
}
private RandomSearchAlgorithm(RandomSearchAlgorithm original, Cloner cloner)
: base(original, cloner) {
singleObjectiveQualityAnalyzer = cloner.Clone(original.singleObjectiveQualityAnalyzer);
evaluationsTerminator = cloner.Clone(original.evaluationsTerminator);
qualityTerminator = cloner.Clone(original.qualityTerminator);
executionTimeTerminator = cloner.Clone(original.executionTimeTerminator);
Initialize();
}
public SimulatedAnnealingImprovementOperator()
: base() {
loop = new SimulatedAnnealingMainLoop();
qualityAnalyzer = new BestAverageWorstQualityAnalyzer();
Parameters.Add(new LookupParameter<IRandom>("Random", "The random number generator to use."));
Parameters.Add(new ConstrainedValueParameter<IMoveGenerator>("MoveGenerator", "The operator used to generate moves to the neighborhood of the current solution."));
Parameters.Add(new ConstrainedValueParameter<IMoveMaker>("MoveMaker", "The operator used to perform a move."));
Parameters.Add(new ConstrainedValueParameter<ISingleObjectiveMoveEvaluator>("MoveEvaluator", "The operator used to evaluate a move."));
Parameters.Add(new ValueLookupParameter<IntValue>("MaximumIterations", "The maximum number of generations which should be processed.", new IntValue(150)));
Parameters.Add(new ValueLookupParameter<IntValue>("InnerIterations", "Number of moves that MultiMoveGenerators should create. This is ignored for Exhaustive- and SingleMoveGenerators.", new IntValue(1500)));
Parameters.Add(new LookupParameter<IntValue>("EvaluatedSolutions", "The number of evaluated moves."));
Parameters.Add(new ValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze the solution.", new MultiAnalyzer()));
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 ConstrainedValueParameter<IDiscreteDoubleValueModifier>("AnnealingOperator", "The operator used to modify the temperature."));
Parameters.Add(new LookupParameter<ResultCollection>("Results", "The variable where the results are stored."));
Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality", "The quality/fitness value of a solution."));
foreach (IDiscreteDoubleValueModifier op in ApplicationManager.Manager.GetInstances<IDiscreteDoubleValueModifier>().OrderBy(x => x.Name))
AnnealingOperatorParameter.ValidValues.Add(op);
ParameterizeAnnealingOperators();
ParameterizeSAMainLoop();
RegisterEventHandlers();
}
private BestAverageWorstQualityAnalyzer(BestAverageWorstQualityAnalyzer original, Cloner cloner)
: base(original, cloner)
{
Initialize();
}
private SimulatedAnnealingImprovementOperator(SimulatedAnnealingImprovementOperator original, Cloner cloner)
: base(original, cloner) {
this.problem = cloner.Clone(original.problem);
this.loop = cloner.Clone(original.loop);
this.qualityAnalyzer = cloner.Clone(original.qualityAnalyzer);
RegisterEventHandlers();
}
public LocalSearch()
: 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<IMoveGenerator>("MoveGenerator", "The operator used to generate moves to the neighborhood of the current solution."));
Parameters.Add(new ConstrainedValueParameter<IMoveMaker>("MoveMaker", "The operator used to perform a move."));
Parameters.Add(new ConstrainedValueParameter<ISingleObjectiveMoveEvaluator>("MoveEvaluator", "The operator used to evaluate a move."));
Parameters.Add(new ValueParameter<IntValue>("MaximumIterations", "The maximum number of generations which should be processed.", new IntValue(1000)));
Parameters.Add(new ValueParameter<IntValue>("SampleSize", "Number of moves that MultiMoveGenerators should create. This is ignored for Exhaustive- and SingleMoveGenerators.", new IntValue(100)));
Parameters.Add(new ValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze the solution and moves.", new MultiAnalyzer()));
RandomCreator randomCreator = new RandomCreator();
SolutionsCreator solutionsCreator = new SolutionsCreator();
VariableCreator variableCreator = new VariableCreator();
ResultsCollector resultsCollector = new ResultsCollector();
LocalSearchMainLoop mainLoop = new LocalSearchMainLoop();
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.CollectedValues.Add(new ValueParameter<DoubleValue>("BestQuality", new DoubleValue(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.MoveMakerParameter.ActualName = MoveMakerParameter.Name;
mainLoop.MoveEvaluatorParameter.ActualName = MoveEvaluatorParameter.Name;
mainLoop.MaximumIterationsParameter.ActualName = MaximumIterationsParameter.Name;
mainLoop.RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
mainLoop.ResultsParameter.ActualName = "Results";
mainLoop.AnalyzerParameter.ActualName = AnalyzerParameter.Name;
mainLoop.EvaluatedMovesParameter.ActualName = "EvaluatedMoves";
mainLoop.IterationsParameter.ActualName = "Iterations";
mainLoop.BestLocalQualityParameter.ActualName = "BestQuality";
moveQualityAnalyzer = new BestAverageWorstQualityAnalyzer();
ParameterizeAnalyzers();
UpdateAnalyzers();
Initialize();
}
private LocalSearch(LocalSearch original, Cloner cloner)
: base(original, cloner) {
moveQualityAnalyzer = cloner.Clone(original.moveQualityAnalyzer);
Initialize();
}
private RAPGA(RAPGA original, Cloner cloner)
: base(original, cloner) {
qualityAnalyzer = cloner.Clone(original.qualityAnalyzer);
populationSizeAnalyzer = cloner.Clone(original.populationSizeAnalyzer);
offspringSuccessAnalyzer = cloner.Clone(original.offspringSuccessAnalyzer);
selectionPressureAnalyzer = cloner.Clone(original.selectionPressureAnalyzer);
Initialize();
}
public LocalSearchImprovementOperator()
: base() {
Parameters.Add(new ConstrainedValueParameter<IMoveGenerator>("MoveGenerator", "The operator used to generate moves to the neighborhood of the current solution."));
Parameters.Add(new ConstrainedValueParameter<IMoveMaker>("MoveMaker", "The operator used to perform a move."));
Parameters.Add(new ConstrainedValueParameter<ISingleObjectiveMoveEvaluator>("MoveEvaluator", "The operator used to evaluate a move."));
Parameters.Add(new ValueLookupParameter<IntValue>("MaximumIterations", "The maximum number of generations which should be processed.", new IntValue(150)));
Parameters.Add(new ValueLookupParameter<IntValue>("SampleSize", "Number of moves that MultiMoveGenerators should create. This is ignored for Exhaustive- and SingleMoveGenerators.", new IntValue(300)));
Parameters.Add(new LookupParameter<IntValue>("EvaluatedSolutions", "The number of evaluated moves."));
Parameters.Add(new ValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze the solution.", new MultiAnalyzer()));
Parameters.Add(new LookupParameter<ResultCollection>("Results", "The name of the collection where the results are stored."));
Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality", "The quality/fitness value of a solution."));
Parameters.Add(new LookupParameter<IRandom>("Random", "The random number generator to use."));
loop = new LocalSearchMainLoop();
((ResultsCollector)((SingleSuccessorOperator)loop.OperatorGraph.InitialOperator).Successor).CollectedValues.Remove(loop.BestLocalQualityParameter.Name);
ParameterizeLSMainLoop();
qualityAnalyzer = new BestAverageWorstQualityAnalyzer();
Analyzer.Operators.Add(qualityAnalyzer);
RegisterEventHandlers();
}
public VariableNeighborhoodSearch()
: base() {
Parameters.Add(new FixedValueParameter<IntValue>("Seed", "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
Parameters.Add(new FixedValueParameter<BoolValue>("SetSeedRandomly", "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
Parameters.Add(new ConstrainedValueParameter<ILocalImprovementOperator>("LocalImprovement", "The local improvement operation"));
Parameters.Add(new ConstrainedValueParameter<IMultiNeighborhoodShakingOperator>("ShakingOperator", "The operator that performs the shaking of solutions."));
Parameters.Add(new FixedValueParameter<IntValue>("MaximumIterations", "The maximum number of iterations which should be processed.", new IntValue(50)));
Parameters.Add(new FixedValueParameter<IntValue>("LocalImprovementMaximumIterations", "The maximum number of iterations which should be performed in the local improvement phase.", new IntValue(50)));
Parameters.Add(new ValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze the solution and moves.", new MultiAnalyzer()));
RandomCreator randomCreator = new RandomCreator();
SolutionsCreator solutionsCreator = new SolutionsCreator();
VariableCreator variableCreator = new VariableCreator();
ResultsCollector resultsCollector = new ResultsCollector();
VariableNeighborhoodSearchMainLoop mainLoop = new VariableNeighborhoodSearchMainLoop();
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 Evaluated Solutions";
variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("Iterations", new IntValue(0)));
variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("EvaluatedSolutions", new IntValue(0)));
variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("CurrentNeighborhoodIndex", new IntValue(0)));
variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("NeighborhoodCount", new IntValue(0)));
variableCreator.Successor = resultsCollector;
resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("Evaluated Solutions", null, "EvaluatedSolutions"));
resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("Iterations"));
resultsCollector.ResultsParameter.ActualName = "Results";
resultsCollector.Successor = mainLoop;
mainLoop.IterationsParameter.ActualName = "Iterations";
mainLoop.CurrentNeighborhoodIndexParameter.ActualName = "CurrentNeighborhoodIndex";
mainLoop.NeighborhoodCountParameter.ActualName = "NeighborhoodCount";
mainLoop.LocalImprovementParameter.ActualName = LocalImprovementParameter.Name;
mainLoop.ShakingOperatorParameter.ActualName = ShakingOperatorParameter.Name;
mainLoop.MaximumIterationsParameter.ActualName = MaximumIterationsParameter.Name;
mainLoop.RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
mainLoop.ResultsParameter.ActualName = "Results";
mainLoop.AnalyzerParameter.ActualName = AnalyzerParameter.Name;
mainLoop.EvaluatedSolutionsParameter.ActualName = "EvaluatedSolutions";
InitializeLocalImprovementOperators();
qualityAnalyzer = new BestAverageWorstQualityAnalyzer();
ParameterizeAnalyzers();
UpdateAnalyzers();
RegisterEventHandlers();
}
public GeneticAlgorithm()
: 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", "The size of the population of solutions.", 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 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 ValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze each generation.", new MultiAnalyzer()));
Parameters.Add(new ValueParameter<IntValue>("MaximumGenerations", "The maximum number of generations which should be processed.", new IntValue(1000)));
RandomCreator randomCreator = new RandomCreator();
SolutionsCreator solutionsCreator = new SolutionsCreator();
SubScopesCounter subScopesCounter = new SubScopesCounter();
ResultsCollector resultsCollector = new ResultsCollector();
GeneticAlgorithmMainLoop mainLoop = new GeneticAlgorithmMainLoop();
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 = resultsCollector;
resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("Evaluated Solutions", null, "EvaluatedSolutions"));
resultsCollector.ResultsParameter.ActualName = "Results";
resultsCollector.Successor = mainLoop;
mainLoop.SelectorParameter.ActualName = SelectorParameter.Name;
mainLoop.CrossoverParameter.ActualName = CrossoverParameter.Name;
mainLoop.ElitesParameter.ActualName = ElitesParameter.Name;
mainLoop.ReevaluateElitesParameter.ActualName = ReevaluateElitesParameter.Name;
mainLoop.MaximumGenerationsParameter.ActualName = MaximumGenerationsParameter.Name;
mainLoop.MutatorParameter.ActualName = MutatorParameter.Name;
mainLoop.MutationProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
mainLoop.RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
mainLoop.AnalyzerParameter.ActualName = AnalyzerParameter.Name;
mainLoop.EvaluatedSolutionsParameter.ActualName = "EvaluatedSolutions";
mainLoop.PopulationSizeParameter.ActualName = PopulationSizeParameter.Name;
mainLoop.ResultsParameter.ActualName = "Results";
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();
qualityAnalyzer = new BestAverageWorstQualityAnalyzer();
ParameterizeAnalyzers();
UpdateAnalyzers();
Initialize();
}
public RandomSearchAlgorithm()
: base() {
#region Add parameters
Parameters.Add(new FixedValueParameter<IntValue>("Seed", "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
Parameters.Add(new FixedValueParameter<BoolValue>("SetSeedRandomly", "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
Parameters.Add(new FixedValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze the solutions each iteration.", new MultiAnalyzer()));
Parameters.Add(new FixedValueParameter<IntValue>("MaximumEvaluatedSolutions", "The number of random solutions the algorithm should evaluate.", new IntValue(1000)));
Parameters.Add(new FixedValueParameter<IntValue>("BatchSize", "The number of random solutions that are evaluated (in parallel) per iteration.", new IntValue(100)));
Parameters.Add(new FixedValueParameter<IntValue>("MaximumIterations", "The number of iterations that the algorithm will run.", new IntValue(10)) { Hidden = true });
Parameters.Add(new FixedValueParameter<MultiTerminator>("Terminator", "The termination criteria that defines if the algorithm should continue or stop.", new MultiTerminator()) { Hidden = true });
#endregion
#region Create operators
var randomCreator = new RandomCreator();
var variableCreator = new VariableCreator() { Name = "Initialize Variables" };
var resultsCollector = new ResultsCollector();
var solutionCreator = new SolutionsCreator() { Name = "Create Solutions" };
var analyzerPlaceholder = new Placeholder() { Name = "Analyzer (Placeholder)" };
var evaluationsCounter = new IntCounter() { Name = "Increment EvaluatedSolutions" };
var subScopesRemover = new SubScopesRemover();
var iterationsCounter = new IntCounter() { Name = "Increment Iterations" };
var terminationOperator = new TerminationOperator();
#endregion
#region Create and parameterize operator graph
OperatorGraph.InitialOperator = randomCreator;
randomCreator.SeedParameter.Value = null;
randomCreator.SeedParameter.ActualName = SeedParameter.Name;
randomCreator.SetSeedRandomlyParameter.Value = null;
randomCreator.SetSeedRandomlyParameter.ActualName = SetSeedRandomlyParameter.Name;
randomCreator.Successor = variableCreator;
variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("Iterations", new IntValue(0)));
variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("EvaluatedSolutions", new IntValue(0)));
variableCreator.Successor = resultsCollector;
resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("Iterations", "The current iteration number."));
resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("EvaluatedSolutions", "The current number of evaluated solutions."));
resultsCollector.Successor = solutionCreator;
solutionCreator.NumberOfSolutionsParameter.ActualName = BatchSizeParameter.Name;
solutionCreator.ParallelParameter.Value.Value = true;
solutionCreator.Successor = evaluationsCounter;
evaluationsCounter.ValueParameter.ActualName = "EvaluatedSolutions";
evaluationsCounter.Increment = null;
evaluationsCounter.IncrementParameter.ActualName = BatchSizeParameter.Name;
evaluationsCounter.Successor = analyzerPlaceholder;
analyzerPlaceholder.OperatorParameter.ActualName = AnalyzerParameter.Name;
analyzerPlaceholder.Successor = subScopesRemover;
subScopesRemover.RemoveAllSubScopes = true;
subScopesRemover.Successor = iterationsCounter;
iterationsCounter.ValueParameter.ActualName = "Iterations";
iterationsCounter.Increment = new IntValue(1);
iterationsCounter.Successor = terminationOperator;
terminationOperator.TerminatorParameter.ActualName = TerminatorParameter.Name;
terminationOperator.ContinueBranch = solutionCreator;
#endregion
#region Create analyzers
singleObjectiveQualityAnalyzer = new BestAverageWorstQualityAnalyzer();
#endregion
#region Create terminators
evaluationsTerminator = new ComparisonTerminator<IntValue>("EvaluatedSolutions", ComparisonType.Less, MaximumEvaluatedSolutionsParameter) { Name = "Evaluated solutions." };
qualityTerminator = new SingleObjectiveQualityTerminator() { Name = "Quality" };
executionTimeTerminator = new ExecutionTimeTerminator(this, new TimeSpanValue(TimeSpan.FromMinutes(5)));
#endregion
#region Parameterize
UpdateAnalyzers();
ParameterizeAnalyzers();
UpdateTerminators();
#endregion
Initialize();
}
private GeneticAlgorithm(GeneticAlgorithm original, Cloner cloner)
: base(original, cloner) {
qualityAnalyzer = cloner.Clone(original.qualityAnalyzer);
Initialize();
}
public OffspringSelectionGeneticAlgorithm()
: 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", "The size of the population of solutions.", 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 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 ValueParameter<IntValue>("MaximumGenerations", "The maximum number of generations which should be processed.", new IntValue(1000)));
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)));
Parameters.Add(new ValueLookupParameter<DoubleValue>("ComparisonFactorUpperBound", "The upper bound of the comparison factor (end).", new DoubleValue(1)));
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<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 each generation.", 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(false)) { Hidden = true });
RandomCreator randomCreator = new RandomCreator();
SolutionsCreator solutionsCreator = new SolutionsCreator();
SubScopesCounter subScopesCounter = new SubScopesCounter();
ResultsCollector resultsCollector = new ResultsCollector();
OffspringSelectionGeneticAlgorithmMainLoop mainLoop = new OffspringSelectionGeneticAlgorithmMainLoop();
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 = resultsCollector;
resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("Evaluated Solutions", "", "EvaluatedSolutions"));
resultsCollector.ResultsParameter.ActualName = "Results";
resultsCollector.Successor = mainLoop;
mainLoop.AnalyzerParameter.ActualName = AnalyzerParameter.Name;
mainLoop.ComparisonFactorModifierParameter.ActualName = ComparisonFactorModifierParameter.Name;
mainLoop.ComparisonFactorParameter.ActualName = "ComparisonFactor";
mainLoop.ComparisonFactorStartParameter.ActualName = ComparisonFactorLowerBoundParameter.Name;
mainLoop.CrossoverParameter.ActualName = CrossoverParameter.Name;
mainLoop.ElitesParameter.ActualName = ElitesParameter.Name;
mainLoop.ReevaluateElitesParameter.ActualName = ReevaluateElitesParameter.Name;
mainLoop.EvaluatedSolutionsParameter.ActualName = "EvaluatedSolutions";
mainLoop.MaximumGenerationsParameter.ActualName = MaximumGenerationsParameter.Name;
mainLoop.MaximumSelectionPressureParameter.ActualName = MaximumSelectionPressureParameter.Name;
mainLoop.MutationProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
mainLoop.MutatorParameter.ActualName = MutatorParameter.Name;
mainLoop.OffspringSelectionBeforeMutationParameter.ActualName = OffspringSelectionBeforeMutationParameter.Name;
mainLoop.RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
mainLoop.ResultsParameter.ActualName = "Results";
mainLoop.SelectorParameter.ActualName = SelectorParameter.Name;
mainLoop.SuccessRatioParameter.ActualName = SuccessRatioParameter.Name;
mainLoop.FillPopulationWithParentsParameter.ActualName = FillPopulationWithParentsParameter.Name;
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();
selectionPressureAnalyzer = new ValueAnalyzer();
successfulOffspringAnalyzer = new SuccessfulOffspringAnalyzer();
ParameterizeAnalyzers();
UpdateAnalyzers();
Initialize();
}
private BestAverageWorstQualityAnalyzer(BestAverageWorstQualityAnalyzer original, Cloner cloner)
: base(original, cloner) {
Initialize();
}
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 OptionalConstrainedValueParameter<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();
}
private AlpsOffspringSelectionGeneticAlgorithm(AlpsOffspringSelectionGeneticAlgorithm original, Cloner cloner)
: base(original, cloner) {
qualityAnalyzer = cloner.Clone(original.qualityAnalyzer);
layerQualityAnalyzer = cloner.Clone(original.layerQualityAnalyzer);
ageAnalyzer = cloner.Clone(original.ageAnalyzer);
layerAgeAnalyzer = cloner.Clone(original.layerAgeAnalyzer);
ageDistributionAnalyzer = cloner.Clone(original.ageDistributionAnalyzer);
layerAgeDistributionAnalyzer = cloner.Clone(original.layerAgeDistributionAnalyzer);
selectionPressureAnalyzer = cloner.Clone(original.selectionPressureAnalyzer);
layerSelectionPressureAnalyzer = cloner.Clone(original.layerSelectionPressureAnalyzer);
currentSuccessRatioAnalyzer = cloner.Clone(original.currentSuccessRatioAnalyzer);
generationsTerminator = cloner.Clone(original.generationsTerminator);
evaluationsTerminator = cloner.Clone(original.evaluationsTerminator);
qualityTerminator = cloner.Clone(original.qualityTerminator);
executionTimeTerminator = cloner.Clone(original.executionTimeTerminator);
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();
}
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 OffspringSelectionEvolutionStrategy(OffspringSelectionEvolutionStrategy original, Cloner cloner)
: base(original, cloner) {
qualityAnalyzer = cloner.Clone(original.qualityAnalyzer);
selectionPressureAnalyzer = cloner.Clone(original.selectionPressureAnalyzer);
Initialize();
}
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 InitializeAnalyzers() {
qualityAnalyzer = new BestAverageWorstQualityAnalyzer();
qualityAnalyzer.ResultsParameter.ActualName = "Results";
ParameterizeAnalyzers();
}
private VariableNeighborhoodSearch(VariableNeighborhoodSearch original, Cloner cloner)
: base(original, cloner) {
qualityAnalyzer = cloner.Clone(original.qualityAnalyzer);
RegisterEventHandlers();
}
private TabuSearch(TabuSearch original, Cloner cloner)
: base(original, cloner) {
moveQualityAnalyzer = cloner.Clone(original.moveQualityAnalyzer);
tabuNeighborhoodAnalyzer = cloner.Clone(original.tabuNeighborhoodAnalyzer);
Initialize();
}
private SASEGASA(SASEGASA original, Cloner cloner)
: base(original, cloner) {
qualityAnalyzer = cloner.Clone(original.qualityAnalyzer);
villageQualityAnalyzer = cloner.Clone(original.villageQualityAnalyzer);
selectionPressureAnalyzer = cloner.Clone(original.selectionPressureAnalyzer);
villageSelectionPressureAnalyzer = cloner.Clone(original.villageSelectionPressureAnalyzer);
successfulOffspringAnalyzer = cloner.Clone(original.successfulOffspringAnalyzer);
Initialize();
}
