private AlpsGeneticAlgorithm CreateAlpsGaSymRegSample()
{
AlpsGeneticAlgorithm alpsGa = new AlpsGeneticAlgorithm();
#region Problem Configuration
var provider = new VladislavlevaInstanceProvider();
var instance = provider.GetDataDescriptors().Single(x => x.Name.StartsWith("Vladislavleva-5 F5"));
var symbRegProblem = new SymbolicRegressionSingleObjectiveProblem();
symbRegProblem.Load(provider.LoadData(instance));
symbRegProblem.MaximumSymbolicExpressionTreeDepth.Value = 35;
symbRegProblem.MaximumSymbolicExpressionTreeLength.Value = 35;
var grammar = (TypeCoherentExpressionGrammar)symbRegProblem.SymbolicExpressionTreeGrammar;
grammar.Symbols.OfType <Exponential>().Single().Enabled = false;
grammar.Symbols.OfType <Logarithm>().Single().Enabled = false;
#endregion
#region Algorithm Configuration
alpsGa.Name = "ALPS Genetic Programming - Symbolic Regression";
alpsGa.Description = "An ALPS-GP to solve a symbolic regression problem (Vladislavleva-5 dataset)";
alpsGa.Problem = symbRegProblem;
SamplesUtils.ConfigureAlpsGeneticAlgorithmParameters <GeneralizedRankSelector, SubtreeCrossover, MultiSymbolicExpressionTreeManipulator>(alpsGa,
numberOfLayers: 1000,
popSize: 100,
mutationRate: 0.25,
elites: 1,
plusSelection: false,
agingScheme: AgingScheme.Polynomial,
ageGap: 15,
ageInheritance: 1.0,
maxGens: 500);
#endregion
return(alpsGa);
}
private AlpsGeneticAlgorithm CreateAlpsGaTspSample()
{
AlpsGeneticAlgorithm alpsGa = new AlpsGeneticAlgorithm();
#region Problem Configuration
var provider = new TSPLIBTSPInstanceProvider();
var instance = provider.GetDataDescriptors().Single(x => x.Name == "ch130");
TravelingSalesmanProblem tspProblem = new TravelingSalesmanProblem();
tspProblem.Load(provider.LoadData(instance));
tspProblem.UseDistanceMatrix.Value = true;
#endregion
#region Algorithm Configuration
alpsGa.Name = "ALPS Genetic Algorithm - TSP";
alpsGa.Description = "An age-layered population structure genetic algorithm which solves the \"ch130\" traveling salesman problem (imported from TSPLIB)";
alpsGa.Problem = tspProblem;
SamplesUtils.ConfigureAlpsGeneticAlgorithmParameters <GeneralizedRankSelector, MultiPermutationCrossover, InversionManipulator>(alpsGa,
numberOfLayers: 1000,
popSize: 100,
mutationRate: 0.05,
elites: 1,
plusSelection: true,
agingScheme: AgingScheme.Polynomial,
ageGap: 20,
ageInheritance: 1.0,
maxGens: 1000);
var checkedCrossovers = new[] { typeof(EdgeRecombinationCrossover), typeof(MaximalPreservativeCrossover), typeof(OrderCrossover2) };
var multiCrossover = (MultiPermutationCrossover)alpsGa.Crossover;
var crossovers = multiCrossover.Operators.Where(c => checkedCrossovers.Any(cc => cc.IsInstanceOfType(c))).ToList();
foreach (var c in multiCrossover.Operators)
{
multiCrossover.Operators.SetItemCheckedState(c, crossovers.Contains(c));
}
#endregion
return(alpsGa);
}
public static void ConfigureAlpsGeneticAlgorithmParameters <S, C, M>(AlpsGeneticAlgorithm ga, int numberOfLayers, int popSize, double mutationRate, int elites, bool plusSelection, AgingScheme agingScheme, int ageGap, double ageInheritance, int maxGens)
where S : ISelector
where C : ICrossover
where M : IManipulator
{
ga.Seed.Value = 0;
ga.SetSeedRandomly.Value = true;
ga.NumberOfLayers.Value = numberOfLayers;
ga.PopulationSize.Value = popSize;
ga.Selector = ga.SelectorParameter.ValidValues.OfType <S>().Single();
ga.Crossover = ga.CrossoverParameter.ValidValues.OfType <C>().Single();
ga.Mutator = ga.MutatorParameter.ValidValues.OfType <M>().Single();
ga.MutationProbability.Value = mutationRate;
ga.Elites.Value = elites;
ga.PlusSelection = plusSelection;
ga.AgingScheme = new EnumValue <AgingScheme>(agingScheme);
ga.AgeGap.Value = ageGap;
ga.AgeInheritance.Value = ageInheritance;
ga.MaximumGenerations = maxGens;
ga.Engine = new ParallelEngine.ParallelEngine();
}
