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);
}
}
public void Can_Run_Robots_Through_Genetic_Processor()
{
RobotGeneticProblem robotGeneticProblem = new RobotGeneticProblem();
// IChildGenerator<Robot> childGenerator = new GenerateRobotsByPairing();
IChildGenerator <Robot> childGenerator = new GenerateRobotsByMutation();
GeneticAlgorithmProcessor <Robot> processor = new GeneticAlgorithmProcessor <Robot>(robotGeneticProblem, childGenerator);
const int populationSize = 300;
var population = processor.GetInitialPopulation(populationSize).ToArray();
var initialFitness = processor.CalculatePopulationFitness(population);
Debug.Print("Initial Fitness: {0}", initialFitness);
for (int i = 0; i < 500; i++)
{
population = processor.GetNextPopulation(population).ToArray();
var nextFitness = processor.CalculatePopulationFitness(population);
var maxFitness = population.Max(x => robotGeneticProblem.GetFitness(x));
Debug.Print("Iteration {0}: avg = {1}, max = {2}", i, nextFitness, maxFitness);
}
}
