public Population(int count, IPopulationGenerator <T> generator, IFitnessEvaluator <T> evaluator)
{
if (count <= 0)
{
throw new ArgumentException($"{nameof(count)} must be greater than 0. Actual: {count}.");
}
if (generator == null)
{
throw new System.ArgumentNullException(nameof(generator));
}
if (evaluator == null)
{
throw new System.ArgumentNullException(nameof(evaluator));
}
TargetSize = count;
// TODO: This really shouldn't be here. We should instead pass the
// population
for (var i = 0; i < count; i++)
{
var value = generator.Generate();
var fitness = evaluator.ComputeFitness(value);
var chromosome = new Chromosome <T>(value, fitness);
Chromosomes.Add(chromosome);
}
}
public TestGeneticAlgorithm(
double mutationProbability,
double crossoverPropability,
int minPopulationSize,
int maxPopulationSize,
IMutation mutationOperator,
ICrossover crossoverOperator,
IFitnessEvaluator <TestWorkload> fitnessEvaluator,
IInitialPopulationCreator initialPopulationCreator,
ITerminationCondition <TestState> terminationCondition,
ISelectionStrategy selectionStrategy,
IReinsertionStrategy reinsertionStrategy,
IDeploymentChromosomeFactory chromosomeFactory,
TestState currentState,
TestWorkload workload,
IRandomProvider randomProvider)
: base(
mutationProbability,
crossoverPropability,
minPopulationSize,
maxPopulationSize,
mutationOperator,
crossoverOperator,
fitnessEvaluator,
initialPopulationCreator,
terminationCondition,
selectionStrategy,
reinsertionStrategy,
chromosomeFactory,
currentState,
workload,
randomProvider)
{
}
public GeneticAlgorithm(
double mutationProbability,
double crossoverPropability,
int minPopulationSize,
int maxPopulationSize,
IMutation mutationOperator,
ICrossover crossoverOperator,
IFitnessEvaluator <TWorkload> fitnessEvaluator,
IInitialPopulationCreator initialPopulationCreator,
ITerminationCondition <TState> terminationCondition,
ISelectionStrategy selectionStrategy,
IReinsertionStrategy reinsertionStrategy,
IDeploymentChromosomeFactory chromosomeFactory,
TState currentState,
TWorkload workload,
IRandomProvider randomProvider)
{
MutationProbability = mutationProbability;
CrossoverProbability = crossoverPropability;
MinPopulationSize = minPopulationSize;
MaxPopulationSize = maxPopulationSize;
MutationOperator = mutationOperator;
CrossoverOperator = crossoverOperator;
FitnessEvaluator = fitnessEvaluator;
InitialPopulationCreator = initialPopulationCreator;
TerminationCondition = terminationCondition;
SelectionStrategyStrategy = selectionStrategy;
ReinsertionStrategy = reinsertionStrategy;
ChromosomeFactory = chromosomeFactory;
CurrentState = currentState;
Workload = workload;
RandomProvider = randomProvider;
}
public GeneticAlgorithmBuilder <U> SetFitnessEvaluator(IFitnessEvaluator <U> fitnessEvaluator)
{
Contract.Requires <ArgumentNullException>(fitnessEvaluator != null, "Argument cannot be null.");
FitnessEvaluator = fitnessEvaluator;
return(this);
}
public Selector(IFitnessEvaluator <string> fitnessEvaluator, int count, int total)
{
Contract.Requires <ArgumentNullException>(fitnessEvaluator != null);
Contract.Requires <ArgumentOutOfRangeException>(count > 0);
Contract.Requires <ArgumentOutOfRangeException>(total > 0);
FitnessEvaluator = fitnessEvaluator;
Count = count;
Total = total;
}
public UniqueChromosomeSelector(IFitnessEvaluator <string> fitnessEvaluator, int count, int total)
{
if (fitnessEvaluator == null)
{
throw new System.ArgumentNullException(nameof(fitnessEvaluator));
}
FitnessEvaluator = fitnessEvaluator;
Count = count;
Total = total;
}
public StringSinglePointCrossOver(Random random, IFitnessEvaluator <string> evaluator)
{
if (random == null)
{
throw new ArgumentNullException($"{nameof(random)}");
}
if (evaluator == null)
{
throw new ArgumentNullException($"{nameof(evaluator)}");
}
Random = random;
Evaluator = evaluator;
}
public Mutator(Func <State <string>, int> mutatorCount, IFitnessEvaluator <string> evaluator)
{
if (mutatorCount == null)
{
throw new ArgumentNullException(nameof(mutatorCount));
}
if (evaluator == null)
{
throw new System.ArgumentNullException(nameof(evaluator));
}
MutatorCount = mutatorCount;
Evaluator = evaluator;
}
/// <summary>
/// Initializes a new instance of the <see cref="GeneticAlgorithmManager"/> class
/// with given <paramref name="fitnessEvaluator"/> and <paramref name="populationSize"/>.
/// </summary>
/// <param name="fitnessEvaluator">Object which has to evaluate fitness values for chromosomes in each population.</param>
/// <param name="populationGenerator">Object which has to generate populations for each generation.</param>
/// <param name="populationSize">The number of chromosomes of population used in the algorithm.</param>
/// <param name="initialChromosomeBrain">The string representation of the initial chromosome.</param>
public GeneticAlgorithmManager(IFitnessEvaluator fitnessEvaluator, IPopulationGenerator populationGenerator = null, int populationSize = 4, string initialChromosomeBrain = null)
{
if (fitnessEvaluator == null)
{
throw new ArgumentNullException("fitnessEvaluator");
}
PopulationSize = populationSize;
Generation = 0;
this.fitnessEvaluator = fitnessEvaluator;
this.fitnessEvaluator.EvaluationFinished += FitnessEvaluator_EvaluationFinished;
this.populationGenerator = populationGenerator ?? new SRMPopulationGenerator(25);
this.initialChromosomeBrain = initialChromosomeBrain;
}
public void SetUp()
{
_mutationProbability = 1;
_crossoverProbability = 1;
_minPopulationSize = 2;
_maxPopulationSize = 4;
_mutationOperator = A.Fake <IMutation>();
_crossoverOperator = A.Fake <ICrossover>();
_fitnessEvaluator = A.Fake <IFitnessEvaluator <TestWorkload> >();
_initialPopulationCreator = A.Fake <IInitialPopulationCreator>();
_terminationCondition = A.Fake <ITerminationCondition <TestState> >();
_selectionStrategy = A.Fake <ISelectionStrategy>();
_reinsertionStrategy = A.Fake <IReinsertionStrategy>();
_deploymentChromosomeFactory = A.Fake <IDeploymentChromosomeFactory>();
_currentState = new TestState();
_workload = new TestWorkload();
_randomProvider = new DefaultRandomProvider();
}
public GeneticAlgorithm(
IPopulationFactory <TGeneSequence> populationFactory,
ITermination <TGeneSequence> termination,
IFitnessEvaluator <TGeneSequence> fitnessEvaluator,
ISelection <TGeneSequence> preservationSelection,
ISelection <TGeneSequence> parentSelection,
ICrossover <TGeneSequence> crossover,
IMutation <TGeneSequence> mutation,
int initialPopulation)
{
_populationFactory = populationFactory;
_termination = termination;
_fitnessEvaluator = fitnessEvaluator;
_preservationSelection = preservationSelection;
_parentSelection = parentSelection;
_crossover = crossover;
_mutation = mutation;
_initialPopulation = initialPopulation;
}
public Population(int count, IPopulationGenerator <T> generator, IFitnessEvaluator <T> evaluator)
{
Contract.Requires <ArgumentOutOfRangeException>(count > 0);
Contract.Requires <ArgumentNullException>(generator != null);
Contract.Requires <ArgumentNullException>(evaluator != null);
TargetSize = count;
Evaluator = evaluator;
// TODO: This really shouldn't be here. We should instead pass the
// population
for (var i = 0; i < count; i++)
{
var value = generator.Generate();
var chromosome = new Chromosome <T>(value);
chromosome.Fitness = evaluator.ComputeFitness(chromosome.Genes);
Chromosomes.Add(chromosome);
}
}
public SinglePointCrossOver(Random random, IFitnessEvaluator <string> evaluator)
{
Random = random;
Evaluator = evaluator;
}
public SinglePointCrossOver(Random random, IFitnessEvaluator <string> evaluator, Func <State <string>, int> crossOverCount)
{
Random = random;
Evaluator = evaluator;
CrossOverCount = crossOverCount;
}
public Optimiser(IFitnessEvaluator fitnessEvaluator)
{
_fitnessEvaluator = fitnessEvaluator;
}
public Optimiser(IFitnessEvaluator fitnessEvaluator)
{
_fitnessEvaluator = fitnessEvaluator;
}