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();
}
private OffspringSuccessAnalyzer(OffspringSuccessAnalyzer original, Cloner cloner) : base(original, cloner)
{
}
public RAPGA()
: 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 ValueParameter <IntValue>("MinimumPopulationSize", "The minimum size of the population of solutions.", new IntValue(2)));
Parameters.Add(new ValueParameter <IntValue>("MaximumPopulationSize", "The maximum size of the population of solutions.", new IntValue(300)));
Parameters.Add(new ValueParameter <DoubleValue>("ComparisonFactor", "The comparison factor.", new DoubleValue(0.0)));
Parameters.Add(new ValueParameter <IntValue>("Effort", "The maximum number of offspring created in each generation.", new IntValue(1000)));
Parameters.Add(new ValueParameter <IntValue>("BatchSize", "The number of children that should be created during one iteration of the offspring creation process.", new IntValue(10)));
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)));
Parameters.Add(new ConstrainedValueParameter <ISolutionSimilarityCalculator>("SimilarityCalculator", "The operator used to calculate the similarity between two solutions."));
RandomCreator randomCreator = new RandomCreator();
SolutionsCreator solutionsCreator = new SolutionsCreator();
SubScopesCounter subScopesCounter = new SubScopesCounter();
ResultsCollector resultsCollector = new ResultsCollector();
RAPGAMainLoop mainLoop = new RAPGAMainLoop();
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();
populationSizeAnalyzer = new PopulationSizeAnalyzer();
offspringSuccessAnalyzer = new OffspringSuccessAnalyzer();
selectionPressureAnalyzer = new SelectionPressureAnalyzer();
ParameterizeAnalyzers();
UpdateAnalyzers();
Initialize();
}
public RAPGA()
: 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 ValueParameter<IntValue>("MinimumPopulationSize", "The minimum size of the population of solutions.", new IntValue(2)));
Parameters.Add(new ValueParameter<IntValue>("MaximumPopulationSize", "The maximum size of the population of solutions.", new IntValue(300)));
Parameters.Add(new ValueParameter<DoubleValue>("ComparisonFactor", "The comparison factor.", new DoubleValue(0.0)));
Parameters.Add(new ValueParameter<IntValue>("Effort", "The maximum number of offspring created in each generation.", new IntValue(1000)));
Parameters.Add(new ValueParameter<IntValue>("BatchSize", "The number of children that should be created during one iteration of the offspring creation process.", new IntValue(10)));
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)));
Parameters.Add(new ConstrainedValueParameter<ISolutionSimilarityCalculator>("SimilarityCalculator", "The operator used to calculate the similarity between two solutions."));
RandomCreator randomCreator = new RandomCreator();
SolutionsCreator solutionsCreator = new SolutionsCreator();
SubScopesCounter subScopesCounter = new SubScopesCounter();
ResultsCollector resultsCollector = new ResultsCollector();
RAPGAMainLoop mainLoop = new RAPGAMainLoop();
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();
populationSizeAnalyzer = new PopulationSizeAnalyzer();
offspringSuccessAnalyzer = new OffspringSuccessAnalyzer();
selectionPressureAnalyzer = new SelectionPressureAnalyzer();
ParameterizeAnalyzers();
UpdateAnalyzers();
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();
}
private OffspringSuccessAnalyzer(OffspringSuccessAnalyzer original, Cloner cloner) : base(original, cloner) { }
