public void SelectChromosomes_NullGeneration_Exception()
        {
            var target = new RouletteWheelSelection();

            ExceptionAssert.IsThrowing(new ArgumentNullException("generation"), () =>
            {
                target.SelectChromosomes(2, null);
            });
        }
        public void SelectChromosomes_InvalidNumber_Exception()
        {
            var target = new RouletteWheelSelection();

            ExceptionAssert.IsThrowing(new ArgumentOutOfRangeException("number", "The number of selected chromosomes should be at least 2."), () =>
            {
                target.SelectChromosomes(-1, null);
            });

            ExceptionAssert.IsThrowing(new ArgumentOutOfRangeException("number", "The number of selected chromosomes should be at least 2."), () =>
            {
                target.SelectChromosomes(0, null);
            });

            ExceptionAssert.IsThrowing(new ArgumentOutOfRangeException("number", "The number of selected chromosomes should be at least 2."), () =>
            {
                target.SelectChromosomes(1, null);
            });
        }
        public void SelectChromosomes_Generation_ChromosomesSelected()
        {
            var target = new RouletteWheelSelection();
            var c1 = MockRepository.GeneratePartialMock<ChromosomeBase> (2);
            c1.Fitness = 0.1;

            var c2 = MockRepository.GeneratePartialMock<ChromosomeBase>(2);
            c2.Fitness = 0.5;

            var c3 = MockRepository.GeneratePartialMock<ChromosomeBase>(2);
            c3.Fitness = 0;

            var c4 = MockRepository.GeneratePartialMock<ChromosomeBase>(2);
            c4.Fitness = 0.7;

            var generation = new Generation (1, new List<IChromosome> () {
                c1, c2, c3, c4
            });

            // Just one selected chromosome is c1.
            FlowAssert.IsAtLeastOneAttemptOk (100, () => {
                var actual = target.SelectChromosomes (2, generation);
                Assert.AreEqual(2, actual.Count);
                Assert.AreEqual(1, actual.Count(c => c.Fitness == 0.1));
            });

            // All selected chromosome is c1.
            FlowAssert.IsAtLeastOneAttemptOk (1000, () => {
                var actual = target.SelectChromosomes (2, generation);
                Assert.AreEqual(2, actual.Count);
                Assert.IsTrue(actual.All(c => c.Fitness == 0.1));
            });

            // Just one selected chromosome is c2.
            FlowAssert.IsAtLeastOneAttemptOk(100, () =>
            {
                var actual = target.SelectChromosomes(2, generation);
                Assert.AreEqual(2, actual.Count);
                Assert.AreEqual(1, actual.Count(c => c.Fitness == 0.5));
            });

            // All selected chromosome is c2.
            FlowAssert.IsAtLeastOneAttemptOk(1000, () =>
            {
                var actual = target.SelectChromosomes(2, generation);
                Assert.AreEqual(2, actual.Count);
                Assert.IsTrue(actual.All(c => c.Fitness == 0.5));
            });

            // None selected chromosome is c3.
            FlowAssert.IsAtLeastOneAttemptOk(100, () =>
            {
                var actual = target.SelectChromosomes(2, generation);
                Assert.AreEqual(2, actual.Count);
                Assert.AreEqual(0, actual.Count(c => c.Fitness == 0.0));
            });

            // Just one selected chromosome is c4.
            FlowAssert.IsAtLeastOneAttemptOk(100, () =>
            {
                var actual = target.SelectChromosomes(2, generation);
                Assert.AreEqual(2, actual.Count);
                Assert.AreEqual(1, actual.Count(c => c.Fitness == 0.7));
            });

            // All selected chromosome is c4.
            FlowAssert.IsAtLeastOneAttemptOk(1000, () =>
            {
                var actual = target.SelectChromosomes(2, generation);
                Assert.AreEqual(2, actual.Count);
                Assert.IsTrue(actual.All(c => c.Fitness == 0.7));
            });
        }
Пример #4
0
        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_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();
                                       });
        }