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);
}
public static void ConfigureOffspringSelectionEvolutionStrategyParameters <R, M, SC, SR, SM>(OffspringSelectionEvolutionStrategy es, int popSize,
double successRatio, double comparisonFactor, double maxSelPres, int parentsPerChild, int maxGens, bool plusSelection)
where R : ICrossover
where M : IManipulator
where SC : IStrategyParameterCreator
where SR : IStrategyParameterCrossover
where SM : IStrategyParameterManipulator
{
es.PopulationSize.Value = popSize;
es.ComparisonFactor.Value = comparisonFactor;
es.SuccessRatio.Value = successRatio;
es.MaximumSelectionPressure.Value = maxSelPres;
es.ParentsPerChild.Value = parentsPerChild;
es.SelectedParents.Value = popSize * parentsPerChild;
es.MaximumGenerations.Value = maxGens;
es.PlusSelection.Value = false;
es.Seed.Value = 0;
es.SetSeedRandomly.Value = true;
es.Recombinator = es.RecombinatorParameter.ValidValues
.OfType <R>()
.Single();
es.Mutator = es.MutatorParameter.ValidValues
.OfType <M>()
.Single();
es.StrategyParameterCreator = es.StrategyParameterCreatorParameter.ValidValues
.OfType <SC>()
.Single();
es.StrategyParameterCrossover = es.StrategyParameterCrossoverParameter.ValidValues
.OfType <SR>()
.Single();
es.StrategyParameterManipulator = es.StrategyParameterManipulatorParameter.ValidValues
.OfType <SM>()
.Single();
es.Engine = new ParallelEngine.ParallelEngine();
}
