public void Cross_ParentsWithTwoGenes_Cross()
{
var target = new OnePointCrossover(0);
var chromosome1 = MockRepository.GenerateStub<ChromosomeBase>(2);
chromosome1.ReplaceGenes(0, new Gene[]
{
new Gene(1),
new Gene(2)
});
chromosome1.Expect(c => c.CreateNew()).Return(MockRepository.GenerateStub<ChromosomeBase>(2));
var chromosome2 = MockRepository.GenerateStub<ChromosomeBase>(2);
chromosome2.ReplaceGenes(0, new Gene[]
{
new Gene(3),
new Gene(4)
});
chromosome2.Expect(c => c.CreateNew()).Return(MockRepository.GenerateStub<ChromosomeBase>(2));
var actual = target.Cross(new List<IChromosome>() { chromosome1, chromosome2 });
Assert.AreEqual(2, actual.Count);
Assert.AreEqual(2, actual[0].Length);
Assert.AreEqual(2, actual[1].Length);
Assert.AreEqual(1, actual[0].GetGene(0).Value);
Assert.AreEqual(4, actual[0].GetGene(1).Value);
Assert.AreEqual(3, actual[1].GetGene(0).Value);
Assert.AreEqual(2, actual[1].GetGene(1).Value);
}
public void Cross_LessGenesThenSwapPoint_Exception()
{
var target = new OnePointCrossover(1);
var chromosome1 = MockRepository.GenerateStub<ChromosomeBase>(2);
var chromosome2 = MockRepository.GenerateStub<ChromosomeBase>(2);
ExceptionAssert.IsThrowing(new ArgumentOutOfRangeException("parents", "The swap point index is 1, but there is only 2 genes. The swap should result at least one gene to each side."), () =>
{
target.Cross(new List<IChromosome>() {
chromosome1,
chromosome2
});
});
}
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 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_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_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_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_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 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);
}
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 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_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();
});
}