private OffspringSelectionEvolutionStrategy(OffspringSelectionEvolutionStrategy original, Cloner cloner)
: base(original, cloner)
{
qualityAnalyzer = cloner.Clone(original.qualityAnalyzer);
selectionPressureAnalyzer = cloner.Clone(original.selectionPressureAnalyzer);
Initialize();
}
private OffspringSelectionEvolutionStrategy CreateOSESGriewankSample() {
OffspringSelectionEvolutionStrategy es = new OffspringSelectionEvolutionStrategy();
#region Problem Configuration
SingleObjectiveTestFunctionProblem problem = new SingleObjectiveTestFunctionProblem();
problem.ProblemSize.Value = 10;
problem.EvaluatorParameter.Value = new GriewankEvaluator();
problem.SolutionCreatorParameter.Value = new UniformRandomRealVectorCreator();
problem.Maximization.Value = false;
problem.Bounds = new DoubleMatrix(new double[,] { { -600, 600 } });
problem.BestKnownQuality.Value = 0;
problem.BestKnownSolutionParameter.Value = new RealVector(10);
problem.Name = "Single Objective Test Function";
problem.Description = "Test function with real valued inputs and a single objective.";
#endregion
#region Algorithm Configuration
es.Name = "Offspring Selection Evolution Strategy - Griewank";
es.Description = "An evolution strategy with offspring selection which solves the 10-dimensional Griewank test function";
es.Problem = problem;
SamplesUtils.ConfigureOffspringSelectionEvolutionStrategyParameters<AverageCrossover, NormalAllPositionsManipulator,
StdDevStrategyVectorCreator, StdDevStrategyVectorCrossover, StdDevStrategyVectorManipulator>(es, 50, 1.0, 0.5, 100, 2, 100, false);
StdDevStrategyVectorCreator strategyCreator = (StdDevStrategyVectorCreator)es.StrategyParameterCreator;
strategyCreator.BoundsParameter.Value = new DoubleMatrix(new double[,] { { 1, 20 } });
StdDevStrategyVectorManipulator strategyManipulator =
(StdDevStrategyVectorManipulator)es.StrategyParameterManipulator;
strategyManipulator.BoundsParameter.Value = new DoubleMatrix(new double[,] { { 1E-12, 30 } });
strategyManipulator.GeneralLearningRateParameter.Value = new DoubleValue(0.22360679774997896);
strategyManipulator.LearningRateParameter.Value = new DoubleValue(0.39763536438352531);
#endregion
return es;
}
private OffspringSelectionEvolutionStrategy(OffspringSelectionEvolutionStrategy original, Cloner cloner)
: base(original, cloner) {
qualityAnalyzer = cloner.Clone(original.qualityAnalyzer);
selectionPressureAnalyzer = cloner.Clone(original.selectionPressureAnalyzer);
Initialize();
}