public void Test_Evolve()
{
Population pop = new Population(1024, 0.8d, 0.1d, 0.05d);
Chromosome[] oldArr = pop.GetPopulation();
// Evolve and get the new population
pop.Evolve();
Chromosome[] newArr = pop.GetPopulation();
// Check the details on the resulting evolved population.
Assert.AreEqual(80, (int) (pop.GetCrossover() * 100));
Assert.AreEqual(10, (int) (pop.GetElitism() * 100));
Assert.AreEqual(5, (int) (pop.GetMutation() * 100));
// Check to ensure that the elitism took.
int elitismCount = (int)Math.Round(1024 * 0.1d);
int counter = 0;
for (int i = 0; i < oldArr.Length; i++)
{
if (Array.BinarySearch(newArr, oldArr[i]) >= 0)
{
++counter;
}
}
// Account for the fact that mating/mutation may cause more than
// just the fixed number of chromosomes to be identical.
Assert.IsTrue(counter >= elitismCount);
Assert.IsTrue(counter < oldArr.Length);
}
public void Test_GetMutation()
{
Population pop = new Population(1024, 0.8d, 0.1d, 0.05d);
Assert.AreEqual(5, (int) (pop.GetMutation() * 100));
pop = new Population(1024, 0.8d, 0.1d, 0.0d);
Assert.AreEqual(0, (int) (pop.GetMutation() * 100));
pop = new Population(1024, 0.8d, 0.1d, 1.0d);
Assert.AreEqual(100, (int) (pop.GetMutation() * 100));
}
public void Test_GetElitism()
{
Population pop = new Population(1024, 0.8d, 0.1d, 0.05d);
Assert.AreEqual(10, (int) (pop.GetElitism() * 100));
pop = new Population(1024, 0.8d, 0.0d, 0.05d);
Assert.AreEqual(0, (int) (pop.GetElitism() * 100));
pop = new Population(1024, 0.8d, 0.99d, 0.05d);
Assert.AreEqual(99, (int) (pop.GetElitism() * 100));
}
public void Test_GetCrossover()
{
Population pop = new Population(1024, 0.8d, 0.1d, 0.05d);
Assert.AreEqual(80, (int) (pop.GetCrossover() * 100));
pop = new Population(1024, 0.0d, 0.1d, 0.05d);
Assert.AreEqual(0, (int) (pop.GetCrossover() * 100));
pop = new Population(1024, 1.0d, 0.1d, 0.05d);
Assert.AreEqual(100, (int) (pop.GetCrossover() * 100));
}
static void Main(string[] args)
{
// The size of the simulation population
const int populationSize = 2048;
// The maximum number of generations for the simulation.
const int maxGenerations = 50000;
// The probability of crossover for any member of the population,
// where 0.0 <= crossoverRatio <= 1.0
const double crossoverRatio = 0.8d;
// The portion of the population that will be retained without change
// between evolutions, where 0.0 <= elitismRatio < 1.0
const double elitismRatio = 0.1d;
// The probability of mutation for any member of the population,
// where 0.0 <= mutationRatio <= 1.0
const double mutationRatio = 0.20d;
// Get the current run time. Not very accurate, but useful for
// some simple reporting.
long startTime = DateTime.Now.Ticks;
// Create the initial population
Population population = new Population(populationSize, crossoverRatio, elitismRatio, mutationRatio);
// Start evolving the population, stopping when the maximum number of
// generations is reached, or when we find a solution.
int i = 0;
Chromosome best = population.GetPopulation()[0];
while ((i++ <= maxGenerations) && (best.GetFitness() != 0))
{
Console.WriteLine("Generation " + i + ": " + best.GetGene());
population.Evolve();
best = population.GetPopulation()[0];
}
// Get the end time for the simulation.
long endTime = DateTime.Now.Ticks;
// Print out some information to the console.
Console.WriteLine("Generation " + i + ": " + best.GetGene());
Console.WriteLine("Total execution time: " + (endTime - startTime) + "ms");
Console.ReadLine();
}
public void Test_GetPopulation()
{
Population pop = new Population(1024, 0.8d, 0.1d, 0.05d);
Chromosome[] arr = pop.GetPopulation();
Assert.AreEqual(1024, arr.Length);
Chromosome[] newArr = new Chromosome[arr.Length];
arr.CopyTo(newArr, 0);
Array.Sort(newArr);
// Assert that the array is actually sorted.
CollectionAssert.AreEqual(arr, newArr);
}
