public void GetIndexFromPopulation_Is_Weighted_Towards_Zero()
{
const int populationSize = 100;
int[] count = new int[populationSize];
TestChildGenerator childGenerator = new TestChildGenerator();
for (int i = 0; i < 1000000; i++)
{
count[childGenerator.GetParentIndex(populationSize)]++;
}
for (int i = 0; i < 100; i++)
{
Debug.Print("{0} : {1}", i, count[i]);
}
int subsequentLargerThanPrevious = 0;
int previous = count[0];
for (int i = 1; i < 100; i++)
{
if (count[i] > previous)
{
++subsequentLargerThanPrevious;
}
else
{
previous = count[i];
}
}
Debug.Print("Out of Order: {0}", subsequentLargerThanPrevious);
Assert.IsTrue(subsequentLargerThanPrevious < 15);
}
public void Processor_Can_Generate_Initial_Population()
{
IGeneticProblem <int> problem = new TestProblem();
IChildGenerator <int> childGenerator = new TestChildGenerator();
GeneticAlgorithmProcessor <int> processor = new GeneticAlgorithmProcessor <int>(problem, childGenerator);
int populationSize = 100;
var initialPopulation = processor.GetInitialPopulation(populationSize);
Assert.AreEqual(populationSize, initialPopulation.Count());
}
public void Processor_Can_Calculate_Population_Fitness()
{
IGeneticProblem <int> problem = new TestProblem();
IChildGenerator <int> childGenerator = new TestChildGenerator();
GeneticAlgorithmProcessor <int> processor = new GeneticAlgorithmProcessor <int>(problem, childGenerator);
int populationSize = 100;
var initialPopulation = processor.GetInitialPopulation(populationSize);
var populationFitness = processor.CalculatePopulationFitness(initialPopulation);
Assert.IsTrue(populationFitness != 0, "Shouldn't happen, or at least, won't normally happen");
}
public void Processor_Can_Improve_On_Each_Generation()
{
IGeneticProblem <int> problem = new TestProblem();
IChildGenerator <int> childGenerator = new TestChildGenerator();
GeneticAlgorithmProcessor <int> processor = new GeneticAlgorithmProcessor <int>(problem, childGenerator);
const int populationSize = 100;
var initialPopulation = processor.GetInitialPopulation(populationSize).ToArray();
var initialFitness = processor.CalculatePopulationFitness(initialPopulation);
var nextPopulation = processor.GetNextPopulation(initialPopulation);
var nextFitness = processor.CalculatePopulationFitness(nextPopulation);
Assert.IsTrue(nextFitness < initialFitness, "Fitness moved from {0} to {1}", initialFitness, nextFitness);
}
public void Processor_Can_Converge_Towards_A_Solution()
{
IGeneticProblem <int> problem = new TestProblem();
IChildGenerator <int> childGenerator = new TestChildGenerator();
GeneticAlgorithmProcessor <int> processor = new GeneticAlgorithmProcessor <int>(problem, childGenerator);
const int populationSize = 10000;
int[] population = processor.GetInitialPopulation(populationSize).ToArray();
var initialFitness = processor.CalculatePopulationFitness(population);
Debug.Print("Initial Fitness: {0}", initialFitness);
for (int i = 0; i < 200; i++)
{
population = processor.GetNextPopulation(population).ToArray();
var nextFitness = processor.CalculatePopulationFitness(population);
Debug.Print("Iteration {0}: {1}", i, nextFitness);
Assert.IsTrue(nextFitness < initialFitness);
}
}
