public GeneticAlgorithm CreateGpLawnMowerSample()
{
GeneticAlgorithm ga = new GeneticAlgorithm();
#region Problem Configuration
var problem = new Problems.GeneticProgramming.LawnMower.Problem();
#endregion
#region Algorithm Configuration
ga.Name = "Genetic Programming - Lawn Mower";
ga.Description = "A standard genetic programming algorithm to solve the lawn mower problem";
ga.Problem = problem;
SamplesUtils
.ConfigureGeneticAlgorithmParameters
<TournamentSelector, SubtreeCrossover, MultiSymbolicExpressionTreeArchitectureManipulator>(
ga, 1000, 1, 50, 0.25, 5);
var mutator = (MultiSymbolicExpressionTreeArchitectureManipulator)ga.Mutator;
mutator.Operators.SetItemCheckedState(mutator.Operators
.OfType <OnePointShaker>()
.Single(), false);
#endregion
return(ga);
}
private GeneticAlgorithm CreateGaVrpSample()
{
GeneticAlgorithm ga = new GeneticAlgorithm();
#region Problem Configuration
VehicleRoutingProblem vrpProblem = new VehicleRoutingProblem();
SolomonFormatInstanceProvider instanceProvider = new SolomonInstanceProvider();
CVRPTWData data = instanceProvider.Import(@"Test Resources\C101.txt", @"Test Resources\C101.opt.txt") as CVRPTWData;
vrpProblem.Load(data);
vrpProblem.Name = "C101 VRP (imported from Solomon)";
vrpProblem.Description = "Represents a Vehicle Routing Problem.";
CVRPTWProblemInstance instance = vrpProblem.ProblemInstance as CVRPTWProblemInstance;
instance.DistanceFactor.Value = 1;
instance.FleetUsageFactor.Value = 100;
instance.OverloadPenalty.Value = 100;
instance.TardinessPenalty.Value = 100;
instance.TimeFactor.Value = 0;
vrpProblem.MaximizationParameter.Value.Value = false;
instance.UseDistanceMatrix.Value = true;
instance.Vehicles.Value = 25;
#endregion
#region Algorithm Configuration
ga.Name = "Genetic Algorithm - VRP";
ga.Description = "A genetic algorithm which solves the \"C101\" vehicle routing problem (imported from Solomon)";
ga.Problem = vrpProblem;
SamplesUtils.ConfigureGeneticAlgorithmParameters <TournamentSelector, MultiVRPSolutionCrossover, MultiVRPSolutionManipulator>(
ga, 100, 1, 1000, 0.05, 3);
var xOver = (MultiVRPSolutionCrossover)ga.Crossover;
foreach (var op in xOver.Operators)
{
xOver.Operators.SetItemCheckedState(op, false);
}
xOver.Operators.SetItemCheckedState(xOver.Operators
.OfType <PotvinRouteBasedCrossover>()
.Single(), true);
xOver.Operators.SetItemCheckedState(xOver.Operators
.OfType <PotvinSequenceBasedCrossover>()
.Single(), true);
var manipulator = (MultiVRPSolutionManipulator)ga.Mutator;
foreach (var op in manipulator.Operators)
{
manipulator.Operators.SetItemCheckedState(op, false);
}
manipulator.Operators.SetItemCheckedState(manipulator.Operators
.OfType <PotvinOneLevelExchangeMainpulator>()
.Single(), true);
manipulator.Operators.SetItemCheckedState(manipulator.Operators
.OfType <PotvinTwoLevelExchangeManipulator>()
.Single(), true);
#endregion
return(ga);
}
private GeneticAlgorithm CreateGaBppSample()
{
GeneticAlgorithm ga = new GeneticAlgorithm();
#region Problem Configuration
var bpp = new PermutationProblem();
#endregion
#region Algorithm Configuration
ga.Name = "Genetic Algorithm - Bin Packing Problem (3D)";
ga.Description = "A genetic algorithm which solves the a 3d bin packing problem instance";
ga.Problem = bpp;
SamplesUtils.ConfigureGeneticAlgorithmParameters <TournamentSelector, PartiallyMatchedCrossover, Swap2Manipulator>(
ga, 300, 1, 50, 0.05, 3);
#endregion
return(ga);
}
// there is no unit test for running the sample because a JDK installation would be necessary (and this is not the case on our build server)
public GeneticAlgorithm CreateGpRobocodeSample()
{
GeneticAlgorithm ga = new GeneticAlgorithm();
#region Problem Configuration
Problem antProblem = new Problem();
#endregion
#region Algorithm Configuration
ga.Name = "Genetic Programming - Robocode Java Source";
ga.Description = "A standard genetic programming algorithm to evolve the java source code for a robocode bot (see http://robocode.sourceforge.net/). An installation of Java SE Developmen Kit (JDK) >= 1.6 is necessary to run this sample.";
ga.Problem = antProblem;
SamplesUtils.ConfigureGeneticAlgorithmParameters <TournamentSelector, SubtreeCrossover, MultiSymbolicExpressionTreeArchitectureManipulator>(
ga, 50, 1, 50, 0.15, 2);
ga.Engine = new SequentialEngine.SequentialEngine();
#endregion
return(ga);
}
private GeneticAlgorithm CreateGaGroupingProblemSample()
{
GeneticAlgorithm ga = new GeneticAlgorithm();
#region Problem Configuration
var problem = new SingleObjectiveProgrammableProblem()
{
ProblemScript = { Code = ProblemCode }
};
problem.ProblemScript.Compile();
#endregion
#region Algorithm Configuration
ga.Name = "Genetic Algorithm - Graph Coloring";
ga.Description = "A genetic algorithm which solves a graph coloring problem using the linear linkage encoding.";
ga.Problem = problem;
SamplesUtils.ConfigureGeneticAlgorithmParameters <TournamentSelector, MultiLinearLinkageCrossover, MultiLinearLinkageManipulator>(
ga, 100, 1, 1000, 0.05, 2);
#endregion
return(ga);
}
private GeneticAlgorithm CreateGaTspSample()
{
GeneticAlgorithm ga = new GeneticAlgorithm();
#region Problem Configuration
var provider = new TSPLIBTSPInstanceProvider();
var instance = provider.GetDataDescriptors().Where(x => x.Name == "ch130").Single();
TravelingSalesmanProblem tspProblem = new TravelingSalesmanProblem();
tspProblem.Load(provider.LoadData(instance));
tspProblem.UseDistanceMatrix.Value = true;
#endregion
#region Algorithm Configuration
ga.Name = "Genetic Algorithm - TSP";
ga.Description = "A genetic algorithm which solves the \"ch130\" traveling salesman problem (imported from TSPLIB)";
ga.Problem = tspProblem;
SamplesUtils.ConfigureGeneticAlgorithmParameters <ProportionalSelector, OrderCrossover2, InversionManipulator>(
ga, 100, 1, 1000, 0.05);
#endregion
return(ga);
}
public GeneticAlgorithm CreateGpArtificialAntSample()
{
GeneticAlgorithm ga = new GeneticAlgorithm();
#region Problem Configuration
Problem antProblem = new Problem();
antProblem.BestKnownQuality = 89;
antProblem.Encoding.TreeDepth = 10;
antProblem.Encoding.TreeLength = 100;
antProblem.Encoding.FunctionDefinitions = 3;
antProblem.Encoding.FunctionArguments = 3;
antProblem.MaxTimeSteps.Value = 600;
#endregion
#region Algorithm Configuration
ga.Name = "Genetic Programming - Artificial Ant";
ga.Description = "A standard genetic programming algorithm to solve the artificial ant problem (Santa-Fe trail)";
ga.Problem = antProblem;
SamplesUtils.ConfigureGeneticAlgorithmParameters <TournamentSelector, SubtreeCrossover, MultiSymbolicExpressionTreeArchitectureManipulator>(
ga, 1000, 1, 50, 0.15, 5);
var mutator = (MultiSymbolicExpressionTreeArchitectureManipulator)ga.Mutator;
mutator.Operators.SetItemCheckedState(mutator.Operators
.OfType <FullTreeShaker>()
.Single(), false);
mutator.Operators.SetItemCheckedState(mutator.Operators
.OfType <OnePointShaker>()
.Single(), false);
mutator.Operators.SetItemCheckedState(mutator.Operators
.OfType <ArgumentDeleter>()
.Single(), false);
mutator.Operators.SetItemCheckedState(mutator.Operators
.OfType <SubroutineDeleter>()
.Single(), false);
#endregion
return(ga);
}
private GeneticAlgorithm CreateGpSymbolicRegressionSample()
{
GeneticAlgorithm ga = new GeneticAlgorithm();
#region Problem Configuration
SymbolicRegressionSingleObjectiveProblem symbRegProblem = new SymbolicRegressionSingleObjectiveProblem();
symbRegProblem.Name = "Tower Symbolic Regression Problem";
symbRegProblem.Description = "Tower Dataset (downloaded from: http://www.symbolicregression.com/?q=towerProblem)";
RegressionRealWorldInstanceProvider provider = new RegressionRealWorldInstanceProvider();
var instance = provider.GetDataDescriptors().Where(x => x.Name.Equals("Tower")).Single();
var towerProblemData = (RegressionProblemData)provider.LoadData(instance);
towerProblemData.TargetVariableParameter.Value = towerProblemData.TargetVariableParameter.ValidValues
.First(v => v.Value == "towerResponse");
towerProblemData.InputVariables.SetItemCheckedState(
towerProblemData.InputVariables.Single(x => x.Value == "x1"), true);
towerProblemData.InputVariables.SetItemCheckedState(
towerProblemData.InputVariables.Single(x => x.Value == "x7"), false);
towerProblemData.InputVariables.SetItemCheckedState(
towerProblemData.InputVariables.Single(x => x.Value == "x11"), false);
towerProblemData.InputVariables.SetItemCheckedState(
towerProblemData.InputVariables.Single(x => x.Value == "x16"), false);
towerProblemData.InputVariables.SetItemCheckedState(
towerProblemData.InputVariables.Single(x => x.Value == "x21"), false);
towerProblemData.InputVariables.SetItemCheckedState(
towerProblemData.InputVariables.Single(x => x.Value == "x25"), false);
towerProblemData.InputVariables.SetItemCheckedState(
towerProblemData.InputVariables.Single(x => x.Value == "towerResponse"), false);
towerProblemData.TrainingPartition.Start = 0;
towerProblemData.TrainingPartition.End = 3136;
towerProblemData.TestPartition.Start = 3136;
towerProblemData.TestPartition.End = 4999;
towerProblemData.Name = "Data imported from towerData.txt";
towerProblemData.Description = "Chemical concentration at top of distillation tower, dataset downloaded from: http://vanillamodeling.com/realproblems.html, best R² achieved with nu-SVR = 0.97";
symbRegProblem.ProblemData = towerProblemData;
// configure grammar
var grammar = new TypeCoherentExpressionGrammar();
grammar.ConfigureAsDefaultRegressionGrammar();
grammar.Symbols.OfType <VariableCondition>().Single().InitialFrequency = 0.0;
var varSymbol = grammar.Symbols.OfType <Variable>().Where(x => !(x is LaggedVariable)).Single();
varSymbol.WeightMu = 1.0;
varSymbol.WeightSigma = 1.0;
varSymbol.WeightManipulatorMu = 0.0;
varSymbol.WeightManipulatorSigma = 0.05;
varSymbol.MultiplicativeWeightManipulatorSigma = 0.03;
var constSymbol = grammar.Symbols.OfType <Constant>().Single();
constSymbol.MaxValue = 20;
constSymbol.MinValue = -20;
constSymbol.ManipulatorMu = 0.0;
constSymbol.ManipulatorSigma = 1;
constSymbol.MultiplicativeManipulatorSigma = 0.03;
symbRegProblem.SymbolicExpressionTreeGrammar = grammar;
// configure remaining problem parameters
symbRegProblem.BestKnownQuality.Value = 0.97;
symbRegProblem.FitnessCalculationPartition.Start = 0;
symbRegProblem.FitnessCalculationPartition.End = 2300;
symbRegProblem.ValidationPartition.Start = 2300;
symbRegProblem.ValidationPartition.End = 3136;
symbRegProblem.RelativeNumberOfEvaluatedSamples.Value = 1;
symbRegProblem.MaximumSymbolicExpressionTreeLength.Value = 150;
symbRegProblem.MaximumSymbolicExpressionTreeDepth.Value = 12;
symbRegProblem.MaximumFunctionDefinitions.Value = 0;
symbRegProblem.MaximumFunctionArguments.Value = 0;
symbRegProblem.EvaluatorParameter.Value = new SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator();
#endregion
#region Algorithm Configuration
ga.Problem = symbRegProblem;
ga.Name = "Genetic Programming - Symbolic Regression";
ga.Description = "A standard genetic programming algorithm to solve a symbolic regression problem (tower dataset)";
SamplesUtils.ConfigureGeneticAlgorithmParameters <TournamentSelector, SubtreeCrossover, MultiSymbolicExpressionTreeManipulator>(
ga, 1000, 1, 50, 0.15, 5);
var mutator = (MultiSymbolicExpressionTreeManipulator)ga.Mutator;
mutator.Operators.OfType <FullTreeShaker>().Single().ShakingFactor = 0.1;
mutator.Operators.OfType <OnePointShaker>().Single().ShakingFactor = 1.0;
ga.Analyzer.Operators.SetItemCheckedState(
ga.Analyzer.Operators
.OfType <SymbolicRegressionSingleObjectiveOverfittingAnalyzer>()
.Single(), false);
ga.Analyzer.Operators.SetItemCheckedState(
ga.Analyzer.Operators
.OfType <SymbolicDataAnalysisAlleleFrequencyAnalyzer>()
.First(), false);
#endregion
return(ga);
}
private GeneticAlgorithm CreateGpSymbolicClassificationSample()
{
GeneticAlgorithm ga = new GeneticAlgorithm();
#region Problem Configuration
SymbolicClassificationSingleObjectiveProblem symbClassProblem = new SymbolicClassificationSingleObjectiveProblem();
symbClassProblem.Name = "Mammography Classification Problem";
symbClassProblem.Description = "Mammography dataset imported from the UCI machine learning repository (http://archive.ics.uci.edu/ml/datasets/Mammographic+Mass)";
UCIInstanceProvider provider = new UCIInstanceProvider();
var instance = provider.GetDataDescriptors().Where(x => x.Name.Equals("Mammography, M. Elter, 2007")).Single();
var mammoData = (ClassificationProblemData)provider.LoadData(instance);
mammoData.TargetVariableParameter.Value = mammoData.TargetVariableParameter.ValidValues
.First(v => v.Value == "Severity");
mammoData.InputVariables.SetItemCheckedState(
mammoData.InputVariables.Single(x => x.Value == "BI-RADS"), false);
mammoData.InputVariables.SetItemCheckedState(
mammoData.InputVariables.Single(x => x.Value == "Age"), true);
mammoData.InputVariables.SetItemCheckedState(
mammoData.InputVariables.Single(x => x.Value == "Shape"), true);
mammoData.InputVariables.SetItemCheckedState(
mammoData.InputVariables.Single(x => x.Value == "Margin"), true);
mammoData.InputVariables.SetItemCheckedState(
mammoData.InputVariables.Single(x => x.Value == "Density"), true);
mammoData.InputVariables.SetItemCheckedState(
mammoData.InputVariables.Single(x => x.Value == "Severity"), false);
mammoData.TrainingPartition.Start = 0;
mammoData.TrainingPartition.End = 800;
mammoData.TestPartition.Start = 800;
mammoData.TestPartition.End = 961;
mammoData.Name = "Data imported from mammographic_masses.csv";
mammoData.Description = "Original dataset: http://archive.ics.uci.edu/ml/datasets/Mammographic+Mass, missing values have been replaced with median values.";
symbClassProblem.ProblemData = mammoData;
// configure grammar
var grammar = new TypeCoherentExpressionGrammar();
grammar.ConfigureAsDefaultClassificationGrammar();
grammar.Symbols.OfType <VariableCondition>().Single().Enabled = false;
foreach (var varSy in grammar.Symbols.OfType <VariableBase>())
{
varSy.VariableChangeProbability = 1.0; // for backwards compatibilty
}
var varSymbol = grammar.Symbols.OfType <Variable>().Single();
varSymbol.WeightMu = 1.0;
varSymbol.WeightSigma = 1.0;
varSymbol.WeightManipulatorMu = 0.0;
varSymbol.WeightManipulatorSigma = 0.05;
varSymbol.MultiplicativeWeightManipulatorSigma = 0.03;
var constSymbol = grammar.Symbols.OfType <Constant>().Single();
constSymbol.MaxValue = 20;
constSymbol.MinValue = -20;
constSymbol.ManipulatorMu = 0.0;
constSymbol.ManipulatorSigma = 1;
constSymbol.MultiplicativeManipulatorSigma = 0.03;
symbClassProblem.SymbolicExpressionTreeGrammar = grammar;
// configure remaining problem parameters
symbClassProblem.BestKnownQuality.Value = 0.0;
symbClassProblem.FitnessCalculationPartition.Start = 0;
symbClassProblem.FitnessCalculationPartition.End = 400;
symbClassProblem.ValidationPartition.Start = 400;
symbClassProblem.ValidationPartition.End = 800;
symbClassProblem.RelativeNumberOfEvaluatedSamples.Value = 1;
symbClassProblem.MaximumSymbolicExpressionTreeLength.Value = 100;
symbClassProblem.MaximumSymbolicExpressionTreeDepth.Value = 10;
symbClassProblem.MaximumFunctionDefinitions.Value = 0;
symbClassProblem.MaximumFunctionArguments.Value = 0;
symbClassProblem.EvaluatorParameter.Value = new SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator();
#endregion
#region Algorithm Configuration
ga.Problem = symbClassProblem;
ga.Name = "Genetic Programming - Symbolic Classification";
ga.Description = "A standard genetic programming algorithm to solve a classification problem (Mammographic+Mass dataset)";
SamplesUtils.ConfigureGeneticAlgorithmParameters <TournamentSelector, SubtreeCrossover, MultiSymbolicExpressionTreeManipulator>(
ga, 1000, 1, 100, 0.15, 5
);
var mutator = (MultiSymbolicExpressionTreeManipulator)ga.Mutator;
mutator.Operators.OfType <FullTreeShaker>().Single().ShakingFactor = 0.1;
mutator.Operators.OfType <OnePointShaker>().Single().ShakingFactor = 1.0;
ga.Analyzer.Operators.SetItemCheckedState(
ga.Analyzer.Operators
.OfType <SymbolicClassificationSingleObjectiveOverfittingAnalyzer>()
.Single(), false);
ga.Analyzer.Operators.SetItemCheckedState(
ga.Analyzer.Operators
.OfType <SymbolicDataAnalysisAlleleFrequencyAnalyzer>()
.First(), false);
#endregion
return(ga);
}
