private EvolutionStrategy CreateEsGriewankSample()
{
EvolutionStrategy es = new EvolutionStrategy();
#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 = "Evolution Strategy - Griewank";
es.Description = "An evolution strategy which solves the 10-dimensional Griewank test function";
es.Problem = problem;
SamplesUtils.ConfigureEvolutionStrategyParameters <AverageCrossover, NormalAllPositionsManipulator,
StdDevStrategyVectorCreator, StdDevStrategyVectorCrossover, StdDevStrategyVectorManipulator>(
es, 20, 500, 2, 200, 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);
}
