public static void Main(String[] args)
{
GeneticSelectionProcess <Population <TestIndividual <Boolean>, Boolean>, TestIndividual <Boolean>,
Chromosome <Boolean>, Boolean, ProcessInformation> .Builder builder =
new GeneticSelectionProcess <Population <TestIndividual <Boolean>, Boolean>, TestIndividual <Boolean>,
Chromosome <Boolean>, Boolean, ProcessInformation> .Builder();
Random rng = new Random();
Int32 genomeSize = 100;
Int32 targetFitness = 90;
Int32 fitnessDeltaTolerance = 10;
Int32 populationSize = 1000;
Double mutationRate = 0.01;
GeneticSelectionProcess <Population <TestIndividual <Boolean>, Boolean>, TestIndividual <Boolean>,
Chromosome <Boolean>, Boolean, ProcessInformation> process = builder
.SetInitialPopulationSize(populationSize).UseDefaultConsoleLogger()
.SetProcessInformationComposer(
new ProcessInformationComposer <Population <TestIndividual <Boolean>, Boolean>, TestIndividual <Boolean>
, Boolean>())
.SetMatingFunction(
new CrossOverMatingFunction <TestIndividual <Boolean>, Chromosome <Boolean>, Boolean>(3))
// new RandomCombinationMatingFunction<
// TestIndividual<Boolean>
// , Chromosome<Boolean>
// , Boolean>())
.SetParentSelector(
new SingleGroupFittestParentsSelector <Population <TestIndividual <Boolean>, Boolean>,
TestIndividual <Boolean>, Boolean>()).SetFitnessFunction(new TestFitnessFunction())
.SetSurvivorSelector(
new ReplaceLeastFitSelector <Population <TestIndividual <Boolean>, Boolean>, TestIndividual <Boolean>,
Boolean>())
.SetPopulationInitializer(
new BooleanRandomPopulationInitializer <Population <TestIndividual <Boolean>, Boolean>,
TestIndividual <Boolean>, Chromosome <Boolean> >(genomeSize))
.AddTerminationCondition(
new FitnessTerminator <Population <TestIndividual <Boolean>, Boolean>, TestIndividual <Boolean>, Boolean
>(targetFitness))
.AddPopulationModifier(
new RandomMutationModifier <Population <TestIndividual <Boolean>, Boolean>, TestIndividual <Boolean>,
Boolean>(mutationRate, () => rng.Next(2) == 1)).Build();
process.Initialize();
process.RunUntilConvergence();
IndividualWithFitness <TestIndividual <Boolean>, Boolean> fittest = process.GetPopulation().GetFittest();
Double fittestFitness = fittest.GetFitness();
Boolean[] fittestGenome = fittest.GetIndividual().GetChromosome().GetGenome();
Console.WriteLine($"Max fitness {fittestFitness}");
Console.WriteLine($"Genome {string.Join("", fittestGenome.Select(b => b ? 1 : 0))}");
Console.WriteLine("Done");
}
public void TrivialMaximizeTruesProcessShouldRun()
{
Random rng = new Random();
Int32 genomeSize = 100;
Int32 targetFitness = 90;
Int32 populationSize = 100;
Double mutationRate = 0.01;
var matingFunctions = new List <IMatingFunction <TestIndividual <Boolean>, Chromosome <Boolean>, Boolean> >
{
new CrossOverMatingFunction <TestIndividual <Boolean>, Chromosome <Boolean>, Boolean>(1)
, new CrossOverMatingFunction <TestIndividual <Boolean>, Chromosome <Boolean>, Boolean>(3)
, new RandomCombinationMatingFunction <TestIndividual <Boolean>, Chromosome <Boolean>, Boolean>()
};
foreach (IMatingFunction <TestIndividual <Boolean>, Chromosome <Boolean>, Boolean> matingFunction in
matingFunctions)
{
var builder =
new GeneticSelectionProcess <Population <TestIndividual <Boolean>, Boolean>, TestIndividual <Boolean>,
Chromosome <Boolean>, Boolean, ProcessInformation> .Builder();
var process = builder.SetInitialPopulationSize(populationSize).UseDefaultConsoleLogger()
.SetProcessInformationComposer(
new ProcessInformationComposer <Population <TestIndividual <Boolean>, Boolean>,
TestIndividual <Boolean>, Boolean>()).SetMatingFunction(matingFunction)
.SetParentSelector(
new SingleGroupFittestParentsSelector <Population <TestIndividual <Boolean>, Boolean>,
TestIndividual <Boolean>, Boolean>()).SetFitnessFunction(new TestFitnessFunction())
.SetSurvivorSelector(
new ReplaceLeastFitSelector <Population <TestIndividual <Boolean>, Boolean>,
TestIndividual <Boolean>, Boolean>())
.SetPopulationInitializer(
new BooleanRandomPopulationInitializer <Population <TestIndividual <Boolean>, Boolean>,
TestIndividual <Boolean>, Chromosome <Boolean> >(genomeSize))
.AddTerminationCondition(
new FitnessTerminator <Population <TestIndividual <Boolean>, Boolean>, TestIndividual <Boolean>,
Boolean>(targetFitness))
.AddPopulationModifier(
new RandomMutationModifier <Population <TestIndividual <Boolean>, Boolean>, TestIndividual <Boolean>
, Boolean>(mutationRate, () => rng.Next(2) == 1)).Build();
process.Initialize();
process.RunUntilConvergence();
IndividualWithFitness <TestIndividual <Boolean>, Boolean> fittest = process.GetPopulation().GetFittest();
Double fittestFitness = fittest.GetFitness();
Boolean[] fittestGenome = fittest.GetIndividual().GetChromosome().GetGenome();
Double expectedFitness = fittestGenome.Select(b => b ? 1 : 0).Sum();
Assert.True(fittestFitness >= 90);
Assert.True(expectedFitness >= 90);
Assert.True(expectedFitness.AreDoublesEqual(fittestFitness));
IList <ProcessInformation> history = process.GetHistory();
Assert.True(history.Count > 1);
Assert.True(fittestFitness.AreDoublesEqual(history[history.Count - 1].FittestFitness));
}
}
/// <summary>
/// Run a single generation of the process.
/// </summary>
/// <exception cref="InvalidOperationException"></exception>
// ReSharper disable always PossibleNullReferenceException
public void RunGeneration()
{
#region Validation
if (_population == null)
{
throw new InvalidOperationException($"{nameof(_population)} was null");
}
if (_survivorSelector == null)
{
throw new InvalidOperationException($"{nameof(_survivorSelector)} was null");
}
if (_populationModifiers == null)
{
throw new InvalidOperationException($"{nameof(_populationModifiers)} was null");
}
if (_parentSelector == null)
{
throw new InvalidOperationException($"{nameof(_parentSelector)} was null");
}
if (_processInformationComposer == null)
{
throw new InvalidOperationException($"{nameof(_processInformationComposer)} was null");
}
#endregion
_isRunning = true;
UInt64 generationCount = _previousProcessInformation?.GetGeneration() ?? 0;
LogDebug($"Starting generation {generationCount}");
// Select parents to produce new generation of offspring.
LogDebug("Selecting parents");
IList <IEnumerable <TIndividual> > parentGroups = _parentSelector.GetParents(_population).ToList();
// Log parents and their fitness.
Int32 parentGroupCount = 0;
foreach (IEnumerable <TIndividual> parentGroup in parentGroups)
{
Double totalFitness = 0;
IList <String> parentStrings = new List <String>();
foreach (TIndividual parent in parentGroup)
{
IndividualWithFitness <TIndividual, TGene> individualParent =
_population.GetIndividualWithFitness(parent.GetGuid());
Double parentFitness = individualParent.GetFitness();
parentStrings.Add($"P {parent.GetGuidStringShort()} F {parentFitness}");
totalFitness += parentFitness;
}
LogInfo($"PG {parentGroupCount}, TF {totalFitness}, " + $"{string.Join(", ", parentStrings)}");
parentGroupCount++;
}
if (parentGroups == null || !parentGroups.Any())
{
throw new InvalidOperationException("Failed to get parents");
}
// Mate selected parents, produce offspring.
LogDebug("Mating parents; creating offspring");
IList <TIndividual> offspring =
parentGroups.Select(parentGroup => _matingFunction.Mate(parentGroup.ToList())).ToList();
// Log unmodified offspring fitness.
foreach (TIndividual individualOffspring in offspring)
{
Double offspringFitness = _fitnessFunction.CalculateFitness(individualOffspring);
LogInfo($"O {individualOffspring.GetGuidStringShort()} F {offspringFitness}");
}
// Add new offspring to population.
LogDebug("Selecting survivors; adding offspring to population");
_survivorSelector.AddOffspring(_population, offspring);
// Apply population modifiers to population.
LogDebug("Applying population modifier");
foreach (IPopulationModifier <TPopulation, TIndividual, TGene> populationModifier in _populationModifiers)
{
_population = populationModifier.ModifyPopulation(_population);
}
// Order by fitness.
LogDebug("Ordering population by fitness");
_population.OrderByFitness(_fitnessFunction);
// Create information object.
TProcessInformation currentGenerationProcessInformation =
_processInformationComposer.ComposeProcessInformation(_previousProcessInformation, _population);
AddToHistory(currentGenerationProcessInformation);
LogInfo($"G{generationCount} TF {currentGenerationProcessInformation.GetTotalFitness()}");
// Check if population has converged according to provided checks.
if (!_hasConverged)
{
if (_terminationConditions.Any(terminationCondition =>
terminationCondition.ShouldTerminate(_population, currentGenerationProcessInformation)))
{
_hasConverged = true;
LogInfo("Population has converged! Termination condition met!");
}
}
// Compose new process information object.
_previousProcessInformation = currentGenerationProcessInformation;
if (!_isRunningLocked)
{
_isRunning = false;
}
}