public OffspringSelectionGeneticAlgorithm CreateGeSymbolicRegressionSample()
{
var ga = new OffspringSelectionGeneticAlgorithm();
#region Problem Configuration
var problem = new HeuristicLab.Problems.GrammaticalEvolution.GESymbolicRegressionSingleObjectiveProblem();
#endregion
#region Algorithm Configuration
ga.Name = "Grammatical Evolution - Symbolic Regression (Poly-10)";
ga.Description = "Grammatical evolution algorithm for solving a symbolic regression problem problem";
ga.Problem = problem;
problem.Load(new PolyTen().GenerateRegressionData());
// must occur after loading problem data because the grammar creates symbols for random constants once the data is loaded
var consts = problem.SymbolicExpressionTreeGrammar.AllowedSymbols.OfType <Constant>().ToList();
foreach (var c in consts)
{
problem.SymbolicExpressionTreeGrammar.RemoveSymbol(c);
}
SamplesUtils.ConfigureOsGeneticAlgorithmParameters <GenderSpecificSelector, Encodings.IntegerVectorEncoding.SinglePointCrossover, Encodings.IntegerVectorEncoding.UniformOnePositionManipulator>(
ga, 1000, 1, 50, 0.05, 200);
#endregion
return(ga);
}
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 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 void RunGeArtificalAntSampleTest()
{
var ga = CreateGeArtificialAntSample();
ga.SetSeedRandomly.Value = false;
SamplesUtils.RunAlgorithm(ga);
}
public void RunGeSymbolicRegressionSampleTest()
{
var ga = CreateGeSymbolicRegressionSample();
ga.SetSeedRandomly.Value = false;
SamplesUtils.RunAlgorithm(ga);
}
public static OffspringSelectionGeneticAlgorithm CreateGpTimeSeriesSample()
{
var problem = new SymbolicTimeSeriesPrognosisSingleObjectiveProblem();
problem.Name = "Symbolic time series prognosis problem (Mackey Glass t=17)";
problem.ProblemData.Name = "Mackey Glass t=17";
problem.MaximumSymbolicExpressionTreeLength.Value = 125;
problem.MaximumSymbolicExpressionTreeDepth.Value = 12;
problem.EvaluatorParameter.Value.HorizonParameter.Value.Value = 10;
foreach (var symbol in problem.SymbolicExpressionTreeGrammar.Symbols)
{
if (symbol is Exponential || symbol is Logarithm)
{
symbol.Enabled = false;
}
else if (symbol is AutoregressiveTargetVariable)
{
symbol.Enabled = true;
var autoRegressiveSymbol = symbol as AutoregressiveTargetVariable;
autoRegressiveSymbol.MinLag = -30;
autoRegressiveSymbol.MaxLag = -1;
}
}
var osga = new OffspringSelectionGeneticAlgorithm();
osga.Name = "Genetic Programming - Time Series Prediction (Mackey-Glass-17)";
osga.Description = "A genetic programming algorithm for creating a time-series model for the Mackey-Glass-17 time series.";
osga.Problem = problem;
SamplesUtils.ConfigureOsGeneticAlgorithmParameters <GenderSpecificSelector, SubtreeCrossover, MultiSymbolicExpressionTreeManipulator>
(osga, popSize: 100, elites: 1, maxGens: 25, mutationRate: 0.15);
osga.MaximumSelectionPressure.Value = 100;
return(osga);
}
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);
}
public void RunAlpsGaSymRegSampleTest()
{
var alpsGa = CreateAlpsGaSymRegSample();
alpsGa.SetSeedRandomly.Value = false;
SamplesUtils.RunAlgorithm(alpsGa);
Assert.AreEqual(265855, SamplesUtils.GetIntResult(alpsGa, "EvaluatedSolutions"));
}
public void RunSimulatedAnnealingRastriginSampleTest()
{
var sa = CreateSimulatedAnnealingRastriginSample();
sa.SetSeedRandomly.Value = false;
SamplesUtils.RunAlgorithm(sa);
Assert.AreEqual(0.00014039606034543795, SamplesUtils.GetDoubleResult(sa, "BestQuality"));
Assert.AreEqual(5000, SamplesUtils.GetIntResult(sa, "EvaluatedMoves"));
}
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);
}
public void RunGaussianProcessRegressionSample()
{
var gpr = CreateGaussianProcessRegressionSample();
gpr.SetSeedRandomly = false;
gpr.Seed = 1618551877;
SamplesUtils.RunAlgorithm(gpr);
Assert.AreEqual(-940.60591737780555, SamplesUtils.GetDoubleResult(gpr, "NegativeLogLikelihood"));
Assert.AreEqual(0.99560909041069334, SamplesUtils.GetDoubleResult(gpr, "Training R²"));
}
public void RunGaussianProcessRegressionSample()
{
var gpr = CreateGaussianProcessRegressionSample();
gpr.SetSeedRandomly = false;
gpr.Seed = 1618551877;
SamplesUtils.RunAlgorithm(gpr);
Assert.AreEqual(-940.70700288855619, SamplesUtils.GetDoubleResult(gpr, "NegativeLogLikelihood"));
Assert.AreEqual(0.99563390794061979, SamplesUtils.GetDoubleResult(gpr, "Training R²"));
}
public void RunGaussianProcessRegressionSample()
{
var gpr = CreateGaussianProcessRegressionSample();
gpr.SetSeedRandomly = false;
gpr.Seed = 1618551877;
SamplesUtils.RunAlgorithm(gpr);
Assert.AreEqual(-940.39914958616748, SamplesUtils.GetDoubleResult(gpr, "NegativeLogLikelihood"));
Assert.AreEqual(0.995614091354263, SamplesUtils.GetDoubleResult(gpr, "Training R²"));
}
public void RunTabuSearchVRPSampleTest()
{
var vrp = CreateTabuSearchVrpSample();
vrp.SetSeedRandomly.Value = false;
SamplesUtils.RunAlgorithm(vrp);
Assert.AreEqual(1473, SamplesUtils.GetDoubleResult(vrp, "BestQuality"));
Assert.AreEqual(2102.1192622950812, SamplesUtils.GetDoubleResult(vrp, "CurrentAverageQuality"));
Assert.AreEqual(4006, SamplesUtils.GetDoubleResult(vrp, "CurrentWorstQuality"));
Assert.AreEqual(119072, SamplesUtils.GetIntResult(vrp, "EvaluatedMoves"));
}
public void RunVnsOpSampleTest()
{
var vns = CreateVnsOpSample();
vns.SetSeedRandomly = false;
SamplesUtils.RunAlgorithm(vns);
Assert.AreEqual(1182, SamplesUtils.GetDoubleResult(vns, "BestQuality"));
Assert.AreEqual(1182, SamplesUtils.GetDoubleResult(vns, "CurrentAverageQuality"));
Assert.AreEqual(1182, SamplesUtils.GetDoubleResult(vns, "CurrentWorstQuality"));
Assert.AreEqual(42651753, SamplesUtils.GetIntResult(vns, "EvaluatedSolutions"));
}
public void RunScatterSearchVRPSampleTest()
{
var ss = CreateScatterSearchVRPSample();
ss.SetSeedRandomly.Value = false;
SamplesUtils.RunAlgorithm(ss);
Assert.AreEqual(828.93686694283383, SamplesUtils.GetDoubleResult(ss, "BestQuality"));
Assert.AreEqual(868.63623986983077, SamplesUtils.GetDoubleResult(ss, "CurrentAverageQuality"));
Assert.AreEqual(1048.8333559209832, SamplesUtils.GetDoubleResult(ss, "CurrentWorstQuality"));
Assert.AreEqual(262622, SamplesUtils.GetIntResult(ss, "EvaluatedSolutions"));
}
public void RunAlpsGaTspSampleTest()
{
var alpsGa = CreateAlpsGaTspSample();
alpsGa.SetSeedRandomly.Value = false;
SamplesUtils.RunAlgorithm(alpsGa);
Assert.AreEqual(7967, SamplesUtils.GetDoubleResult(alpsGa, "BestQuality"));
Assert.AreEqual(17565.174444444445, SamplesUtils.GetDoubleResult(alpsGa, "CurrentAverageQuality"));
Assert.AreEqual(50295, SamplesUtils.GetDoubleResult(alpsGa, "CurrentWorstQuality"));
Assert.AreEqual(621900, SamplesUtils.GetIntResult(alpsGa, "EvaluatedSolutions"));
}
public void RunEsGriewankSampleTest()
{
var es = CreateEsGriewankSample();
es.SetSeedRandomly.Value = false;
SamplesUtils.RunAlgorithm(es);
Assert.AreEqual(0, SamplesUtils.GetDoubleResult(es, "BestQuality"));
Assert.AreEqual(0, SamplesUtils.GetDoubleResult(es, "CurrentAverageQuality"));
Assert.AreEqual(0, SamplesUtils.GetDoubleResult(es, "CurrentWorstQuality"));
Assert.AreEqual(100020, SamplesUtils.GetIntResult(es, "EvaluatedSolutions"));
}
public void RunRAPGASchedulingSampleTest()
{
var rapga = CreateRAPGASchedulingSample();
rapga.SetSeedRandomly.Value = false;
SamplesUtils.RunAlgorithm(rapga);
Assert.AreEqual(988.00, SamplesUtils.GetDoubleResult(rapga, "BestQuality"));
Assert.AreEqual(988.00, SamplesUtils.GetDoubleResult(rapga, "CurrentAverageQuality"));
Assert.AreEqual(988.00, SamplesUtils.GetDoubleResult(rapga, "CurrentWorstQuality"));
Assert.AreEqual(27100, SamplesUtils.GetIntResult(rapga, "EvaluatedSolutions"));
}
public void RunGaVrpSampleTest()
{
var ga = CreateGaVrpSample();
ga.SetSeedRandomly.Value = false;
SamplesUtils.RunAlgorithm(ga);
Assert.AreEqual(1828.9368669428338, SamplesUtils.GetDoubleResult(ga, "BestQuality"));
Assert.AreEqual(1830.1444308908331, SamplesUtils.GetDoubleResult(ga, "CurrentAverageQuality"));
Assert.AreEqual(1871.7128510304112, SamplesUtils.GetDoubleResult(ga, "CurrentWorstQuality"));
Assert.AreEqual(99100, SamplesUtils.GetIntResult(ga, "EvaluatedSolutions"));
}
public void RunOSESGriewankSampleTest()
{
var es = CreateOSESGriewankSample();
es.SetSeedRandomly.Value = false;
SamplesUtils.RunAlgorithm(es);
Assert.AreEqual(1.80366417E-07, SamplesUtils.GetDoubleResult(es, "BestQuality"), 1.0E-15);
Assert.AreEqual(4.84418627E-07, SamplesUtils.GetDoubleResult(es, "CurrentAverageQuality"), 1.0E-15);
Assert.AreEqual(9.20629802E-07, SamplesUtils.GetDoubleResult(es, "CurrentWorstQuality"), 1.0E-15);
Assert.AreEqual(39750, SamplesUtils.GetIntResult(es, "EvaluatedSolutions"));
}
public void RunGaGroupingProblemSampleTest()
{
var ga = CreateGaGroupingProblemSample();
ga.SetSeedRandomly.Value = false;
SamplesUtils.RunAlgorithm(ga);
Assert.AreEqual(127, SamplesUtils.GetDoubleResult(ga, "BestQuality"));
Assert.AreEqual(129, 38, SamplesUtils.GetDoubleResult(ga, "CurrentAverageQuality"));
Assert.AreEqual(132, SamplesUtils.GetDoubleResult(ga, "CurrentWorstQuality"));
Assert.AreEqual(99100, SamplesUtils.GetIntResult(ga, "EvaluatedSolutions"));
}
public void RunIslandGaTspSampleTest()
{
var ga = CreateIslandGaTspSample();
ga.SetSeedRandomly.Value = false;
SamplesUtils.RunAlgorithm(ga);
Assert.AreEqual(9918, SamplesUtils.GetDoubleResult(ga, "BestQuality"));
Assert.AreEqual(10324.64, SamplesUtils.GetDoubleResult(ga, "CurrentAverageQuality"));
Assert.AreEqual(11823, SamplesUtils.GetDoubleResult(ga, "CurrentWorstQuality"));
Assert.AreEqual(495500, SamplesUtils.GetIntResult(ga, "EvaluatedSolutions"));
}
public void RunTabuSearchTspSampleTest()
{
var ts = CreateTabuSearchTspSample();
ts.SetSeedRandomly.Value = false;
SamplesUtils.RunAlgorithm(ts);
Assert.AreEqual(6294, SamplesUtils.GetDoubleResult(ts, "BestQuality"));
Assert.AreEqual(7380.0386666666664, SamplesUtils.GetDoubleResult(ts, "CurrentAverageQuality"));
Assert.AreEqual(8328, SamplesUtils.GetDoubleResult(ts, "CurrentWorstQuality"));
Assert.AreEqual(750000, SamplesUtils.GetIntResult(ts, "EvaluatedMoves"));
}
public void RunGpArtificialAntSampleTest()
{
var ga = CreateGpArtificialAntSample();
ga.SetSeedRandomly.Value = false;
SamplesUtils.RunAlgorithm(ga);
Assert.AreEqual(81, SamplesUtils.GetDoubleResult(ga, "BestQuality"));
Assert.AreEqual(48.19, SamplesUtils.GetDoubleResult(ga, "CurrentAverageQuality"));
Assert.AreEqual(0, SamplesUtils.GetDoubleResult(ga, "CurrentWorstQuality"));
Assert.AreEqual(50950, SamplesUtils.GetIntResult(ga, "EvaluatedSolutions"));
}
public void RunLocalSearchKnapsackSampleTest()
{
var ls = CreateLocalSearchKnapsackSample();
ls.SetSeedRandomly.Value = false;
SamplesUtils.RunAlgorithm(ls);
Assert.AreEqual(345, SamplesUtils.GetDoubleResult(ls, "BestQuality"));
Assert.AreEqual(340.70731707317071, SamplesUtils.GetDoubleResult(ls, "CurrentAverageQuality"));
Assert.AreEqual(337, SamplesUtils.GetDoubleResult(ls, "CurrentWorstQuality"));
Assert.AreEqual(82000, SamplesUtils.GetIntResult(ls, "EvaluatedMoves"));
}
public void RunGaBppSampleTest()
{
var ga = CreateGaBppSample();
ga.SetSeedRandomly.Value = false;
SamplesUtils.RunAlgorithm(ga);
Assert.AreEqual(0.59369488300408157, SamplesUtils.GetDoubleResult(ga, "BestQuality"), 1E-6);
Assert.AreEqual(0.51834350243004124, SamplesUtils.GetDoubleResult(ga, "CurrentAverageQuality"), 1E-6);
Assert.AreEqual(0.36227753691428577, SamplesUtils.GetDoubleResult(ga, "CurrentWorstQuality"), 1E-6);
Assert.AreEqual(15250, SamplesUtils.GetIntResult(ga, "EvaluatedSolutions"));
}
public void RunGpTimeSeriesSampleTest()
{
var osga = CreateGpTimeSeriesSample();
osga.SetSeedRandomly.Value = false;
SamplesUtils.RunAlgorithm(osga);
Assert.AreEqual(0.020952753415199643, SamplesUtils.GetDoubleResult(osga, "BestQuality"), 1E-8);
Assert.AreEqual(0.023220938866319357, SamplesUtils.GetDoubleResult(osga, "CurrentAverageQuality"), 1E-8);
Assert.AreEqual(0.023716788824595391, SamplesUtils.GetDoubleResult(osga, "CurrentWorstQuality"), 1E-8);
Assert.AreEqual(48200, SamplesUtils.GetIntResult(osga, "EvaluatedSolutions"));
}
public void RunGpMultiplexerSampleTest()
{
var osga = CreateGpMultiplexerSample();
osga.SetSeedRandomly.Value = false;
SamplesUtils.RunAlgorithm(osga);
Assert.AreEqual(1856, SamplesUtils.GetDoubleResult(osga, "BestQuality"), 1E-8);
Assert.AreEqual(1784.76, SamplesUtils.GetDoubleResult(osga, "CurrentAverageQuality"), 1E-8);
Assert.AreEqual(1536, SamplesUtils.GetDoubleResult(osga, "CurrentWorstQuality"), 1E-8);
Assert.AreEqual(66900, SamplesUtils.GetIntResult(osga, "EvaluatedSolutions"));
}
public void RunGpTimeSeriesSampleTest()
{
var osga = CreateGpTimeSeriesSample();
osga.SetSeedRandomly.Value = false;
SamplesUtils.RunAlgorithm(osga);
Assert.AreEqual(0.015441526903606416, SamplesUtils.GetDoubleResult(osga, "BestQuality"), 1E-8);
Assert.AreEqual(0.017420834241279298, SamplesUtils.GetDoubleResult(osga, "CurrentAverageQuality"), 1E-8);
Assert.AreEqual(0.065195703753298972, SamplesUtils.GetDoubleResult(osga, "CurrentWorstQuality"), 1E-8);
Assert.AreEqual(92000, SamplesUtils.GetIntResult(osga, "EvaluatedSolutions"));
}