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 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 void Run_Once_CallsRightMethods()
{
var deploymentModel = CreateDeploymentModel();
A.CallTo(() => _terminationCondition.HasReached(null, null)).WithAnyArguments().ReturnsNextFromSequence(false, true);
_deploymentChromosomeFactory = new DeploymentChromosomeFactory();
_initialPopulationCreator = new CloneInitialPopulationCreator();
_selectionStrategy = new EliteSelection();
_crossoverOperator = new IdCrossover();
_mutationOperator = new IdMutation();
_reinsertionStrategy = new EliteReinsertion();
ConstructGeneticAlgorithm();
var bestDeploymentModel = _testGeneticAlgorithm.Run(deploymentModel);
Assert.AreEqual(1, _currentState.Count);
}
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();
}
