public DemoPlayerInputStrategy()
{
var population = new Population (6, 6, new DemoPlayerChromosome ());
var fitness = new DemoPlayerFitness ();
var selection = new EliteSelection ();
var crossover = new OnePointCrossover (0);
var mutation = new UniformMutation (true);
m_ga = new GeneticAlgorithm (
population,
fitness,
selection,
crossover,
mutation);
m_ga.MutationProbability = 0.5f;
m_ga.GenerationRan += (sender, e) => {
m_bestChromossome = m_ga.BestChromosome as DemoPlayerChromosome;
if(m_bestChromossome.AvoidAliensProjectilesProbability > 0.9f) {
HorizontalDirection = 1f;
}
};
m_ga.Start ();
SHThread.PingPong (.01f, 0, 1, (t) => {
m_ga.Termination = new GenerationNumberTermination (m_ga.GenerationsNumber + 1);
m_ga.Resume();
return true;
});
}
public void Evolve_ManyGenerations_Fast()
{
var selection = new EliteSelection();
var crossover = new UniformCrossover();
var mutation = new UniformMutation(true);
var chromosome = new AutoConfigChromosome();
var targetChromosome = new TspChromosome(10);
var targetFitness = new TspFitness(10, 0, 100, 0, 100);
var fitness = new AutoConfigFitness(targetFitness, targetChromosome);
fitness.PopulationMinSize = 20;
fitness.PopulationMaxSize = 20;
fitness.Termination = new TimeEvolvingTermination(TimeSpan.FromSeconds(5));
var population = new Population(10, 10, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.TaskExecutor = new SmartThreadPoolTaskExecutor()
{
MinThreads = 10,
MaxThreads = 20
};
ga.Termination = new GenerationNumberTermination(2);
ga.Start();
Assert.NotNull(ga.BestChromosome);
}
public void Evolve_ManyGenerations_Fast()
{
var selection = new EliteSelection();
var crossover = new ThreeParentCrossover();
var mutation = new UniformMutation(true);
var fitness = new FunctionBuilderFitness(
new FunctionBuilderInput(
new double[] { 1, 2, 3 },
6)
,
new FunctionBuilderInput(
new double[] { 2, 3, 4 },
24)
);
var chromosome = new FunctionBuilderChromosome(fitness.AvailableOperations, 5);
var population = new Population(100, 200, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new FitnessThresholdTermination(0);
ga.Start();
var bestChromosome = ga.BestChromosome as FunctionBuilderChromosome;
Assert.AreEqual(0.0, bestChromosome.Fitness.Value);
var actual = fitness.GetFunctionResult(
bestChromosome.BuildFunction(),
new FunctionBuilderInput(new double[] { 3, 4, 5 }, 60)
);
Assert.AreEqual(60.0, actual);
}
static void Main(string[] args)
{
var selection = new EliteSelection();
var crossover = new OnePointCrossover(0);
var mutation = new UniformMutation(true);
var fitness = new Issue1Fitness();
var chromosome = new Issue1Chromosome();
var population = new Population(50, 50, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new GenerationNumberTermination(100);
Console.WriteLine("GA running...");
ga.Start();
Console.WriteLine("GA done in {0} generations.", ga.GenerationsNumber);
var bestChromosome = ga.BestChromosome as Issue1Chromosome;
Console.WriteLine("Best solution found is X:{0}, Y:{1} with {2} fitness.", bestChromosome.X, bestChromosome.Y, bestChromosome.Fitness);
Console.ReadKey();
}
public void Mutate_InvalidIndexes_Exception()
{
var target = new UniformMutation (0, 3);
var chromosome = MockRepository.GenerateStub<ChromosomeBase>(3);
chromosome.ReplaceGenes(0, new Gene[]
{
new Gene(1),
new Gene(1),
new Gene(1)
});
chromosome.Expect(c => c.GenerateGene(0)).Return(new Gene(0));
RandomizationProvider.Current = MockRepository.GenerateMock<IRandomization> ();
ExceptionAssert.IsThrowing (new MutationException (target, "The chromosome has no gene on index 3. The chromosome genes lenght is 3."), () => {
target.Mutate (chromosome, 1);
});
chromosome.VerifyAllExpectations ();
RandomizationProvider.Current.VerifyAllExpectations ();
}
public void Mutate_Indexes_RandomIndexes()
{
var target = new UniformMutation(0, 2);
var chromosome = MockRepository.GenerateStub<ChromosomeBase>(3);
chromosome.ReplaceGenes(0, new Gene[]
{
new Gene(1),
new Gene(1),
new Gene(1)
});
chromosome.Expect(c => c.GenerateGene(0)).Return(new Gene(0));
chromosome.Expect(c => c.GenerateGene(2)).Return(new Gene(10));
RandomizationProvider.Current = MockRepository.GenerateMock<IRandomization>();
target.Mutate(chromosome, 1);
Assert.AreEqual(0, chromosome.GetGene(0).Value);
Assert.AreEqual(1, chromosome.GetGene(1).Value);
Assert.AreEqual(10, chromosome.GetGene(2).Value);
chromosome.VerifyAllExpectations();
RandomizationProvider.Current.VerifyAllExpectations();
}
public void Start_ParallelManyGenerations_Optimization()
{
var taskExecutor = new SmartThreadPoolTaskExecutor();
taskExecutor.MinThreads = 100;
taskExecutor.MaxThreads = 100;
var selection = new EliteSelection();
var crossover = new OnePointCrossover(1);
var mutation = new UniformMutation();
var chromosome = new ChromosomeStub();
FlowAssert.IsAtLeastOneAttemptOk(20, () =>
{
var target = new GeneticAlgorithm(new Population(100, 150, chromosome),
new FitnessStub() { SupportsParallel = true }, selection, crossover, mutation);
target.TaskExecutor = taskExecutor;
Assert.AreEqual(GeneticAlgorithmState.NotStarted, target.State);
Assert.IsFalse(target.IsRunning);
target.Start();
Assert.AreEqual(GeneticAlgorithmState.TerminationReached, target.State);
Assert.IsFalse(target.IsRunning);
Assert.IsTrue(target.Population.CurrentGeneration.Chromosomes.Count >= 100);
Assert.IsTrue(target.Population.CurrentGeneration.Chromosomes.Count <= 150);
Assert.IsNotNull(target.Population.BestChromosome);
Assert.IsTrue(target.Population.BestChromosome.Fitness >= 0.9);
Assert.IsTrue(target.Population.Generations.Count > 0);
});
FlowAssert.IsAtLeastOneAttemptOk(20, () =>
{
var target = new GeneticAlgorithm(new Population(100, 150, chromosome),
new FitnessStub() { SupportsParallel = true }, selection, crossover, mutation);
target.TaskExecutor = taskExecutor;
target.Start();
Assert.IsTrue(target.Population.CurrentGeneration.Chromosomes.Count >= 100);
Assert.IsTrue(target.Population.CurrentGeneration.Chromosomes.Count <= 150);
Assert.IsNotNull(target.Population.BestChromosome);
Assert.IsTrue(target.Population.BestChromosome.Fitness >= 0.9);
Assert.IsTrue(target.Population.Generations.Count > 0);
});
}
public void Start_NotParallelManyGenerations_Optimization()
{
var selection = new EliteSelection();
var crossover = new OnePointCrossover(2);
var mutation = new UniformMutation();
var chromosome = new ChromosomeStub();
var target = new GeneticAlgorithm(new Population(50, 50, chromosome),
new FitnessStub() { SupportsParallel = false }, selection, crossover, mutation);
Assert.IsInstanceOf<EliteSelection>(target.Selection);
Assert.IsInstanceOf<OnePointCrossover>(target.Crossover);
Assert.IsInstanceOf<UniformMutation>(target.Mutation);
target.Termination = new GenerationNumberTermination(25);
Assert.AreEqual(GeneticAlgorithmState.NotStarted, target.State);
Assert.IsFalse(target.IsRunning);
target.Start();
Assert.AreEqual(GeneticAlgorithmState.TerminationReached, target.State);
Assert.IsFalse(target.IsRunning);
Assert.AreEqual(25, target.Population.Generations.Count);
var lastFitness = 0.0;
foreach (var g in target.Population.Generations)
{
Assert.GreaterOrEqual(g.BestChromosome.Fitness.Value, lastFitness);
lastFitness = g.BestChromosome.Fitness.Value;
}
Assert.GreaterOrEqual(lastFitness, 0.8);
Assert.AreEqual(lastFitness, target.BestChromosome.Fitness);
}
public void Resume_TerminationReachedAndTerminationExtend_Resumed()
{
var selection = new EliteSelection();
var crossover = new OnePointCrossover(2);
var mutation = new UniformMutation();
var chromosome = new ChromosomeStub();
var target = new GeneticAlgorithm(new Population(100, 199, chromosome),
new FitnessStub() { SupportsParallel = false }, selection, crossover, mutation);
target.Termination = new GenerationNumberTermination(100);
target.Start();
Assert.AreEqual(target.Population.Generations.Count, 100);
var timeEvolving = target.TimeEvolving.Ticks;
Assert.AreEqual(GeneticAlgorithmState.TerminationReached, target.State);
Assert.IsFalse(target.IsRunning);
target.Termination = new GenerationNumberTermination(200);
target.Resume();
Assert.AreEqual(target.Population.Generations.Count, 200);
Assert.Less(timeEvolving, target.TimeEvolving.Ticks);
Assert.AreEqual(GeneticAlgorithmState.TerminationReached, target.State);
Assert.IsFalse(target.IsRunning);
target.Termination = new GenerationNumberTermination(300);
target.Resume();
Assert.AreEqual(target.Population.Generations.Count, 300);
Assert.Less(timeEvolving, target.TimeEvolving.Ticks);
Assert.AreEqual(GeneticAlgorithmState.TerminationReached, target.State);
Assert.IsFalse(target.IsRunning);
}
public void Resume_TerminationReachedAndTerminationNotChanged_Exception()
{
var selection = new EliteSelection();
var crossover = new OnePointCrossover(2);
var mutation = new UniformMutation();
var chromosome = new ChromosomeStub();
var target = new GeneticAlgorithm(new Population(100, 199, chromosome),
new FitnessStub() { SupportsParallel = false }, selection, crossover, mutation);
target.Termination = new GenerationNumberTermination(10);
target.Start();
Assert.AreEqual(10, target.Population.Generations.Count);
var timeEvolving = target.TimeEvolving.Ticks;
Assert.AreEqual(GeneticAlgorithmState.TerminationReached, target.State);
Assert.IsFalse(target.IsRunning);
ExceptionAssert.IsThrowing(new InvalidOperationException("Attempt to resume a genetic algorithm with a termination (GenerationNumberTermination (HasReached: True)) already reached. Please, specify a new termination or extend the current one."), () =>
{
target.Resume();
});
}
public void Resume_Stopped_Resumed()
{
var selection = new EliteSelection();
var crossover = new OnePointCrossover(2);
var mutation = new UniformMutation();
var chromosome = new ChromosomeStub();
var target = new GeneticAlgorithm(new Population(100, 199, chromosome),
new FitnessStub() { SupportsParallel = false }, selection, crossover, mutation);
target.Termination = new TimeEvolvingTermination(TimeSpan.FromMilliseconds(10000));
target.TaskExecutor = new SmartThreadPoolTaskExecutor();
var stoppedCount = 0;
target.Stopped += (e, a) =>
{
Assert.AreEqual(GeneticAlgorithmState.Stopped, target.State);
Assert.IsFalse(target.IsRunning);
stoppedCount++;
};
Parallel.Invoke(
() => target.Start(),
() =>
{
Thread.Sleep(500);
target.Stop();
});
Thread.Sleep(2000);
Parallel.Invoke(
() => target.Resume(),
() =>
{
Thread.Sleep(2000);
Assert.AreEqual(GeneticAlgorithmState.Resumed, target.State);
Assert.IsTrue(target.IsRunning);
});
Assert.AreEqual(1, stoppedCount);
}
public void Resume_NotStarted_Exception()
{
var selection = new EliteSelection();
var crossover = new OnePointCrossover(2);
var mutation = new UniformMutation();
var chromosome = new ChromosomeStub();
var target = new GeneticAlgorithm(new Population(100, 199, chromosome),
new FitnessStub() { SupportsParallel = false }, selection, crossover, mutation);
ExceptionAssert.IsThrowing(new InvalidOperationException("Attempt to resume a genetic algorithm which was not yet started."), () =>
{
target.Resume();
});
}
public void Start_NotParallelManyGenerations_Fast()
{
var selection = new EliteSelection();
var crossover = new OnePointCrossover(2);
var mutation = new UniformMutation();
var chromosome = new ChromosomeStub();
var target = new GeneticAlgorithm(new Population(100, 199, chromosome),
new FitnessStub() { SupportsParallel = false }, selection, crossover, mutation);
target.Termination = new GenerationNumberTermination(100);
TimeAssert.LessThan(30000, () =>
{
target.Start();
});
Assert.AreEqual(100, target.Population.Generations.Count);
Assert.Greater(target.TimeEvolving.TotalMilliseconds, 1);
}
static void Main(string[] args)
{
Console.ForegroundColor = ConsoleColor.DarkGreen;
Console.WriteLine("GeneticSharp - ConsoleApp");
Console.ResetColor();
Console.WriteLine("Select the sample:");
Console.WriteLine("1) TSP (Travelling Salesman Problem)");
Console.WriteLine("2) Ghostwriter");
var sampleNumber = Console.ReadLine();
ISampleController sampleController = null;
switch (sampleNumber)
{
case "1":
sampleController = new TspSampleController(20);
break;
case "2":
sampleController = new GhostwriterSampleController();
break;
default:
return;
}
var selection = new EliteSelection();
var crossover = new UniformCrossover();
var mutation = new UniformMutation(true);
var fitness = sampleController.CreateFitness();
var population = new Population(50, 70, sampleController.CreateChromosome());
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.MutationProbability = 0.4f;
ga.Termination = new FitnessStagnationTermination(100);
ga.TaskExecutor = new SmartThreadPoolTaskExecutor()
{
MinThreads = 25,
MaxThreads = 50
};
ga.GenerationRan += delegate
{
Console.CursorLeft = 0;
Console.CursorTop = 5;
var bestChromosome = ga.Population.BestChromosome;
Console.WriteLine("Generations: {0}", ga.Population.GenerationsNumber);
Console.WriteLine("Fitness: {0:n4}", bestChromosome.Fitness);
Console.WriteLine("Time: {0}", ga.TimeEvolving);
sampleController.Draw(bestChromosome);
};
try
{
ga.Start();
}
catch (Exception ex)
{
Console.ForegroundColor = ConsoleColor.DarkRed;
Console.WriteLine();
Console.WriteLine("Error: {0}", ex.Message);
Console.ResetColor();
Console.ReadKey();
return;
}
Console.ForegroundColor = ConsoleColor.DarkGreen;
Console.WriteLine();
Console.WriteLine("Evolved.");
Console.ResetColor();
Console.ReadKey();
}
public void Start_UsingAllConfigurationCombinationsAvailable_AllRun()
{
var selections = SelectionService.GetSelectionNames();
var crossovers = CrossoverService.GetCrossoverNames();
var mutations = MutationService.GetMutationNames();
var reinsertions = ReinsertionService.GetReinsertionNames();
var chromosome = new OrderedChromosomeStub();
foreach (var s in selections)
{
foreach (var c in crossovers)
{
foreach (var m in mutations)
{
foreach (var r in reinsertions)
{
var selection = SelectionService.CreateSelectionByName(s);
var crossover = CrossoverService.CreateCrossoverByName(c);
var mutation = MutationService.CreateMutationByName(m);
var reinsertion = ReinsertionService.CreateReinsertionByName(r);
if (crossover.IsOrdered ^ mutation.IsOrdered)
{
continue;
}
if (crossover.ParentsNumber > crossover.ChildrenNumber && !reinsertion.CanExpand)
{
continue;
}
if (mutation is UniformMutation)
{
mutation = new UniformMutation(1);
}
var target = new GeneticAlgorithm(
new Population(50, 50, chromosome.Clone())
{
GenerationStrategy = new TrackingGenerationStrategy()
},
new FitnessStub() { SupportsParallel = false },
selection,
crossover,
mutation);
target.Reinsertion = reinsertion;
target.Termination = new GenerationNumberTermination(25);
target.CrossoverProbability = reinsertion.CanExpand ? 0.75f : 1f;
target.Start();
Assert.AreEqual(25, target.Population.Generations.Count);
}
}
}
}
}
public void Start_ThreeParentCrossover_KeepsMinSizePopulation()
{
var selection = new EliteSelection();
var crossover = new ThreeParentCrossover();
var mutation = new UniformMutation();
var chromosome = new ChromosomeStub();
var target = new GeneticAlgorithm(new Population(100, 199, chromosome),
new FitnessStub() { SupportsParallel = false }, selection, crossover, mutation);
target.Termination = new GenerationNumberTermination(100);
target.Start();
Assert.AreEqual(100, target.Population.Generations.Count);
Assert.IsTrue(target.Population.Generations.All(g => g.Chromosomes.Count >= 100));
}
public void Start_TerminationReached_TerminationReachedEventRaised()
{
var selection = new EliteSelection();
var crossover = new OnePointCrossover(2);
var mutation = new UniformMutation();
var chromosome = new ChromosomeStub();
var target = new GeneticAlgorithm(new Population(100, 199, chromosome),
new FitnessStub() { SupportsParallel = false }, selection, crossover, mutation);
target.Termination = new GenerationNumberTermination(1);
var raised = false;
target.TerminationReached += (e, a) =>
{
raised = true;
};
target.Start();
Assert.IsTrue(raised);
}
public void Start_ParallelGAs_Fast()
{
// GA 1
var selection1 = new EliteSelection();
var crossover1 = new OnePointCrossover(2);
var mutation1 = new UniformMutation();
var chromosome1 = new ChromosomeStub();
var ga1 = new GeneticAlgorithm(new Population(100, 199, chromosome1),
new FitnessStub() { SupportsParallel = false }, selection1, crossover1, mutation1);
ga1.Termination = new GenerationNumberTermination(1000);
// GA 2
var selection2 = new EliteSelection();
var crossover2 = new OnePointCrossover(2);
var mutation2 = new UniformMutation();
var chromosome2 = new ChromosomeStub();
var ga2 = new GeneticAlgorithm(new Population(100, 199, chromosome2),
new FitnessStub() { SupportsParallel = false }, selection2, crossover2, mutation2);
ga2.Termination = new GenerationNumberTermination(1000);
Parallel.Invoke(
() => ga1.Start(),
() => ga2.Start());
Assert.AreEqual(1000, ga1.Population.Generations.Count);
Assert.AreEqual(1000, ga2.Population.Generations.Count);
}
public void Stop_Started_Stopped()
{
var selection = new EliteSelection();
var crossover = new OnePointCrossover(2);
var mutation = new UniformMutation();
var chromosome = new ChromosomeStub();
var target = new GeneticAlgorithm(new Population(100, 199, chromosome),
new FitnessStub() { SupportsParallel = false }, selection, crossover, mutation);
target.Termination = new GenerationNumberTermination(10000);
Parallel.Invoke(
() => target.Start(),
() =>
{
Thread.Sleep(10);
Assert.AreEqual(GeneticAlgorithmState.Started, target.State);
Assert.IsTrue(target.IsRunning);
target.Stop();
Thread.Sleep(30);
Assert.AreEqual(GeneticAlgorithmState.Stopped, target.State);
Assert.IsFalse(target.IsRunning);
});
Assert.Less(target.Population.Generations.Count, 10000);
Assert.Greater(target.TimeEvolving.TotalMilliseconds, 8.8);
}
public void Start_ManyCalls_NewEvolutions()
{
RandomizationProvider.Current = new BasicRandomization();
var selection = new EliteSelection();
var crossover = new OnePointCrossover(2);
var mutation = new UniformMutation();
var chromosome = new ChromosomeStub();
var target = new GeneticAlgorithm(new Population(100, 199, chromosome),
new FitnessStub() { SupportsParallel = false }, selection, crossover, mutation);
target.Termination = new GenerationNumberTermination(1000);
Assert.AreEqual(GeneticAlgorithmState.NotStarted, target.State);
Assert.IsFalse(target.IsRunning);
target.Start();
Assert.AreEqual(GeneticAlgorithmState.TerminationReached, target.State);
Assert.IsFalse(target.IsRunning);
var lastTimeEvolving = target.TimeEvolving.Ticks;
Assert.AreEqual(1000, target.Population.Generations.Count);
Assert.Greater(target.TimeEvolving.TotalMilliseconds, 1);
target.Start();
Assert.AreEqual(GeneticAlgorithmState.TerminationReached, target.State);
Assert.IsFalse(target.IsRunning);
Assert.AreEqual(1000, target.Population.Generations.Count);
Assert.AreNotEqual(lastTimeEvolving, target.TimeEvolving.Ticks);
target.Start();
Assert.AreEqual(GeneticAlgorithmState.TerminationReached, target.State);
Assert.IsFalse(target.IsRunning);
Assert.AreEqual(1000, target.Population.Generations.Count);
Assert.AreNotEqual(lastTimeEvolving, target.TimeEvolving.Ticks);
}
public void Start_ParallelManySlowFitness_Timeout()
{
var taskExecutor = new SmartThreadPoolTaskExecutor();
taskExecutor.MinThreads = 100;
taskExecutor.MaxThreads = 100;
taskExecutor.Timeout = TimeSpan.FromMilliseconds(1000);
var selection = new RouletteWheelSelection();
var crossover = new OnePointCrossover(1);
var mutation = new UniformMutation();
var chromosome = new ChromosomeStub();
var target = new GeneticAlgorithm(new Population(100, 150, chromosome),
new FitnessStub() { SupportsParallel = true, ParallelSleep = 1500 }, selection, crossover, mutation);
target.TaskExecutor = taskExecutor;
ExceptionAssert.IsThrowing(new TimeoutException("The fitness evaluation rech the 00:00:01 timeout."), () =>
{
target.Start();
});
Assert.IsFalse(target.IsRunning);
Assert.AreEqual(GeneticAlgorithmState.Stopped, target.State);
}
public void Start_ManyCallsTerminationChanged_NewEvolutions()
{
var selection = new EliteSelection();
var crossover = new OnePointCrossover(2);
var mutation = new UniformMutation();
var chromosome = new ChromosomeStub();
var target = new GeneticAlgorithm(new Population(100, 199, chromosome),
new FitnessStub() { SupportsParallel = false }, selection, crossover, mutation);
target.Termination = new GenerationNumberTermination(100);
target.Start();
var lastTimeEvolving = target.TimeEvolving.TotalMilliseconds;
Assert.AreEqual(100, target.Population.Generations.Count);
Assert.Greater(target.TimeEvolving.TotalMilliseconds, 1);
Assert.Less(target.TimeEvolving.TotalMilliseconds, 1000);
Assert.AreEqual(GeneticAlgorithmState.TerminationReached, target.State);
Assert.IsFalse(target.IsRunning);
target.Termination = new GenerationNumberTermination(50);
target.Start();
Assert.AreEqual(50, target.Population.Generations.Count);
Assert.Less(target.TimeEvolving.TotalMilliseconds, lastTimeEvolving);
lastTimeEvolving = target.TimeEvolving.TotalMilliseconds;
Assert.AreEqual(GeneticAlgorithmState.TerminationReached, target.State);
Assert.IsFalse(target.IsRunning);
target.Termination = new GenerationNumberTermination(25);
target.Start();
Assert.AreEqual(25, target.Population.Generations.Count);
Assert.Less(target.TimeEvolving.TotalMilliseconds, lastTimeEvolving);
Assert.AreEqual(GeneticAlgorithmState.TerminationReached, target.State);
Assert.IsFalse(target.IsRunning);
}
public void Start_InvalidFitnessEvaluateResult_Exception()
{
var selection = new RouletteWheelSelection();
var crossover = new OnePointCrossover(1);
var mutation = new UniformMutation();
var chromosome = new ChromosomeStub();
var fitness = MockRepository.GenerateMock<IFitness>();
fitness.Expect(f => f.Evaluate(null)).IgnoreArguments().Return(1.1);
var target = new GeneticAlgorithm(
new Population(20, 20, chromosome),
fitness, selection, crossover, mutation);
ExceptionAssert.IsThrowing(new FitnessException(fitness, "The {0}.Evaluate returns a fitness with value 1.1. The fitness value should be between 0.0 and 1.0.".With(fitness.GetType())), () =>
{
target.Start();
});
fitness = MockRepository.GenerateMock<IFitness>();
fitness.Expect(f => f.Evaluate(null)).IgnoreArguments().Return(-0.1);
target = new GeneticAlgorithm(
new Population(20, 20, chromosome),
fitness, selection, crossover, mutation);
ExceptionAssert.IsThrowing(new FitnessException(fitness, "The {0}.Evaluate returns a fitness with value -0.1. The fitness value should be between 0.0 and 1.0.".With(fitness.GetType())), () =>
{
target.Start();
});
}