private static async Task RunAlgorithmAsync()
{
SimpleGeneticAlgorithm algorithm = new SimpleGeneticAlgorithm
{
MinimumEnvironmentSize = 1,
FitnessEvaluator = new FitnessEvaluator
{
EvaluationMode = FitnessEvaluationMode.Minimize,
TargetBinary = "010101010101"
},
GeneticEntitySeed = new BinaryStringEntity
{
MaximumStartingLength = 10,
MinimumStartingLength = 5
},
PopulationSeed = new SimplePopulation
{
MinimumPopulationSize = 100
},
SelectionOperator = new FitnessProportionateSelectionOperator
{
SelectionBasedOnFitnessType = FitnessType.Raw
},
CrossoverOperator = new VariableSinglePointCrossoverOperator
{
CrossoverRate = 0.8
},
MutationOperator = new BinaryStringMutationOperator
{
MutationRate = 0.01
},
Terminator = new FitnessTargetTerminator
{
FitnessTarget = 0,
FitnessType = FitnessType.Raw
}
};
algorithm.GenerationCreated += Algorithm_GenerationCreated;
await algorithm.InitializeAsync();
await algorithm.RunAsync();
IEnumerable <GeneticEntity> top10Entities =
algorithm.Environment.Populations[0].Entities.GetEntitiesSortedByFitness(
FitnessType.Raw, FitnessEvaluationMode.Minimize)
.Reverse()
.Take(10);
Console.WriteLine();
Console.WriteLine("Top 10 entities:");
foreach (GeneticEntity entity in top10Entities)
{
Console.WriteLine("Bits: " + entity.Representation + ", Fitness: " + entity.RawFitnessValue);
}
Console.ReadLine();
}
public virtual void SetUp()
{
Problem = new ArtificialAntProblem
{
AppleField = AppleField.ReadFieldFromStrings(Fields.SimpleField)
};
Target = new SimpleGeneticAlgorithm(Problem)
{
GenerationSize = 200,
RandomGenerator = new RandomGenerator(0)
};
}
public ViewModel()
{
SimpleGeneticAlgorithm algorithm = new SimpleGeneticAlgorithm
{
MinimumEnvironmentSize = 3,
FitnessEvaluator = new FitnessEvaluator(),
GeneticEntitySeed = new BinaryStringEntity
{
MinimumStartingLength = 20,
MaximumStartingLength = 20,
IsFixedSize = true
},
PopulationSeed = new SimplePopulation
{
MinimumPopulationSize = 100
},
SelectionOperator = new FitnessProportionateSelectionOperator
{
SelectionBasedOnFitnessType = FitnessType.Raw
},
CrossoverOperator = new SinglePointCrossoverOperator
{
CrossoverRate = 0.8
},
MutationOperator = new UniformBitMutationOperator
{
MutationRate = 0.01
},
//Terminator = new GenerationalTerminator
//{
// FinalGeneration = 5
//}
};
algorithm.Metrics.Add(new MeanFitness());
algorithm.Metrics.Add(new MaximumFitness());
algorithm.GenerationCreated += Algorithm_GenerationCreated;
this.Context = new ExecutionContext(algorithm);
}
public async Task SimpleGeneticAlgorithm_CreateNextGeneration_NullPopulation()
{
SimpleGeneticAlgorithm algorithm = new SimpleGeneticAlgorithm();
PrivateObject accessor = new PrivateObject(algorithm);
await Assert.ThrowsAsync <ArgumentNullException>(() => (Task)accessor.Invoke("CreateNextGenerationAsync", (Population)null));
}
public async Task SimpleGeneticAlgorithm_CreateNextGeneration_Async()
{
SimpleGeneticAlgorithm algorithm = new SimpleGeneticAlgorithm
{
FitnessEvaluator = new MockFitnessEvaluator(),
GeneticEntitySeed = new MockEntity(),
PopulationSeed = new SimplePopulation(),
ElitismStrategy = new MockElitismStrategy
{
ElitistRatio = .1
},
SelectionOperator = new MockSelectionOperator
{
SelectionBasedOnFitnessType = FitnessType.Scaled
},
CrossoverOperator = new MockCrossoverOperator
{
CrossoverRate = 1
},
MutationOperator = new MockMutationOperator
{
MutationRate = 1
}
};
algorithm.ElitismStrategy = new MockElitismStrategy {
ElitistRatio = .1
};
algorithm.ElitismStrategy.Initialize(algorithm);
algorithm.SelectionOperator = new MockSelectionOperator {
SelectionBasedOnFitnessType = FitnessType.Scaled
};
algorithm.SelectionOperator.Initialize(algorithm);
algorithm.CrossoverOperator = new MockCrossoverOperator {
CrossoverRate = 1
};
algorithm.CrossoverOperator.Initialize(algorithm);
algorithm.MutationOperator = new MockMutationOperator {
MutationRate = 1
};
algorithm.MutationOperator.Initialize(algorithm);
PrivateObject accessor = new PrivateObject(algorithm);
SimplePopulation population = GetPopulation(algorithm, 10);
List <GeneticEntity> originalEntities = new List <GeneticEntity>(population.Entities);
int prevPopCount = population.Entities.Count;
await(Task) accessor.Invoke("CreateNextGenerationAsync", population);
// Find the number of entities in the new population that were in the original population
int actualElitistEntitiesCount = population.Entities.Count(e => originalEntities.Contains(e));
Assert.Equal(1, actualElitistEntitiesCount);
Assert.Equal(1, ((MockElitismStrategy)algorithm.ElitismStrategy).GetElitistGeneticEntitiesCallCount);
Assert.Equal(1, ((MockSelectionOperator)algorithm.SelectionOperator).DoSelectCallCount);
Assert.Equal(4, ((MockCrossoverOperator)algorithm.CrossoverOperator).DoCrossoverCallCount);
Assert.Equal(9, ((MockMutationOperator)algorithm.MutationOperator).DoMutateCallCount);
Assert.Equal(prevPopCount, population.Entities.Count);
}
