static void Main(string[] args)
{
Logger logger = new Logger();
logger.Log("Simulation, initialization");
Console.WriteLine("initialize Simulation using following values:");
GeneticParameters options = new GeneticParameters()
{
MaxPopulation = 200,
MaxGeneration = 100,
MutationChance = 0.7,
StartAcceptanceRatio = 0.2
};
/*{
* maxPopulation = 200,
* maxGeneration = 30,
* startAcceptanceRatio = 0.6,
* gridSize = 20,
* mutationChance = 0.2
* }*/
logger.Log(options.ToString());
var startTime = DateTimeOffset.UtcNow;
try
{
GeneticTerrainGenerator generator = new GeneticTerrainGenerator(options, logger);
logger.Log("Begin Simulation");
//THAR BE DRAGONZ
Algorithm best = generator.RunSimulation();
logger.Log("End Simulation");
logger.Log(best.ToString());
/*
* logger.Log("Rendering...");
* Source.Render(
* Source.CreatePictureFromGraph(20, 20, best.RootNode)
* , 20, 20);
*/
}
finally
{
var elapsedTime = DateTimeOffset.UtcNow.Subtract(startTime);
logger.Log($"{nameof(elapsedTime)}:{elapsedTime}");
logger.Print();
}
Console.Read();
}
private void CreateGeneticParameters()
{
geneticParameters = new GeneticParameters
{
generationsCount = 100,
populationSize = 100,
crossoverProb = 0.8f,
mutationProb = 0.5f,
selectionType = SelectionType.TournamentSelection,
tournamentSize = 5
};
}
private static GeneticRunner RunnerSetup(CommandLineOptions commandLineOptions, string source)
{
var @params = new GeneticParameters
{
SequenceLength = 5,
MinPopulation = 20,
MaxPopulation = 30,
Generations = 50,
MetricsKind = commandLineOptions.ChoosenMetrics,
Source = source
};
var runner = new GeneticRunner(@params);
return(runner);
}
public IIndividual <GeneKey, GeneValue>[] Selection(GeneticParameters geneticParameters)
{
switch (geneticParameters.selectionType)
{
case SelectionType.TournamentSelection:
return(TournamentSelection((int)(geneticParameters.crossoverProb * populationSize),
geneticParameters.tournamentSize));
case SelectionType.RouletteSelection:
return(RouletteSelection((int)(geneticParameters.crossoverProb * populationSize)));
default:
return(OptimizedRouletteSelection((int)(geneticParameters.crossoverProb * populationSize)));
}
}
public void NelderOtherHeuristics()
{
int thinningPeriod = 4;
int treeCount = 75;
#if DEBUG
treeCount = 25;
#endif
PlotsWithHeight nelder = PublicApi.GetNelder();
OrganonConfiguration configuration = OrganonTest.CreateOrganonConfiguration(new OrganonVariantNwo());
configuration.Treatments.Harvests.Add(new ThinByIndividualTreeSelection(thinningPeriod));
OrganonStand stand = nelder.ToOrganonStand(configuration, 20, 130.0F, treeCount);
stand.PlantingDensityInTreesPerHectare = TestConstant.NelderReplantingDensityInTreesPerHectare;
Objective landExpectationValue = new Objective()
{
IsLandExpectationValue = true,
PlanningPeriods = 9
};
Objective volume = new Objective()
{
PlanningPeriods = landExpectationValue.PlanningPeriods
};
HeuristicParameters defaultParameters = new HeuristicParameters()
{
UseScaledVolume = false
};
GeneticParameters geneticParameters = new GeneticParameters(treeCount)
{
PopulationSize = 7,
MaximumGenerations = 5,
UseScaledVolume = defaultParameters.UseScaledVolume
};
GeneticAlgorithm genetic = new GeneticAlgorithm(stand, configuration, landExpectationValue, geneticParameters);
TimeSpan geneticRuntime = genetic.Run();
GreatDeluge deluge = new GreatDeluge(stand, configuration, volume, defaultParameters)
{
RainRate = 5,
LowerWaterAfter = 9,
StopAfter = 10
};
deluge.RandomizeTreeSelection(TestConstant.Default.SelectionPercentage);
TimeSpan delugeRuntime = deluge.Run();
RecordTravel recordTravel = new RecordTravel(stand, configuration, landExpectationValue, defaultParameters)
{
StopAfter = 10
};
recordTravel.RandomizeTreeSelection(TestConstant.Default.SelectionPercentage);
TimeSpan recordRuntime = recordTravel.Run();
SimulatedAnnealing annealer = new SimulatedAnnealing(stand, configuration, volume, defaultParameters)
{
Iterations = 100
};
annealer.RandomizeTreeSelection(TestConstant.Default.SelectionPercentage);
TimeSpan annealerRuntime = annealer.Run();
TabuParameters tabuParameters = new TabuParameters()
{
UseScaledVolume = defaultParameters.UseScaledVolume
};
TabuSearch tabu = new TabuSearch(stand, configuration, landExpectationValue, tabuParameters)
{
Iterations = 7,
//Jump = 2,
MaximumTenure = 5
};
tabu.RandomizeTreeSelection(TestConstant.Default.SelectionPercentage);
TimeSpan tabuRuntime = tabu.Run();
ThresholdAccepting thresholdAcceptor = new ThresholdAccepting(stand, configuration, volume, defaultParameters);
thresholdAcceptor.IterationsPerThreshold.Clear();
thresholdAcceptor.Thresholds.Clear();
thresholdAcceptor.IterationsPerThreshold.Add(10);
thresholdAcceptor.Thresholds.Add(1.0F);
thresholdAcceptor.RandomizeTreeSelection(TestConstant.Default.SelectionPercentage);
TimeSpan acceptorRuntime = thresholdAcceptor.Run();
RandomGuessing random = new RandomGuessing(stand, configuration, volume, defaultParameters)
{
Iterations = 4
};
TimeSpan randomRuntime = random.Run();
configuration.Treatments.Harvests.Clear();
configuration.Treatments.Harvests.Add(new ThinByPrescription(thinningPeriod));
PrescriptionParameters prescriptionParameters = new PrescriptionParameters()
{
Maximum = 60.0F,
Minimum = 50.0F,
Step = 10.0F,
UseScaledVolume = defaultParameters.UseScaledVolume
};
PrescriptionEnumeration enumerator = new PrescriptionEnumeration(stand, configuration, landExpectationValue, prescriptionParameters);
TimeSpan enumerationRuntime = enumerator.Run();
// heuristics assigned to volume optimization
this.Verify(deluge);
this.Verify(annealer);
this.Verify(thresholdAcceptor);
this.Verify(random);
// heuristics assigned to net present value optimization
this.Verify(genetic);
this.Verify(enumerator);
this.Verify(recordTravel);
this.Verify(tabu);
HeuristicSolutionDistribution distribution = new HeuristicSolutionDistribution(1, thinningPeriod, treeCount);
distribution.AddRun(annealer, annealerRuntime, defaultParameters);
distribution.AddRun(deluge, delugeRuntime, defaultParameters);
distribution.AddRun(thresholdAcceptor, acceptorRuntime, defaultParameters);
distribution.AddRun(genetic, geneticRuntime, defaultParameters);
distribution.AddRun(enumerator, enumerationRuntime, defaultParameters);
distribution.AddRun(recordTravel, recordRuntime, defaultParameters);
distribution.AddRun(tabu, tabuRuntime, defaultParameters);
distribution.AddRun(random, randomRuntime, defaultParameters);
distribution.OnRunsComplete();
}
