/// <summary>
/// createPopulation - Creates the initial population
/// </summary>
public static void createPopulation()
{
int[] initParent = new int[NUM_QUEENS];
Random r = new Random();
// Populating the population with random initial parents
for (int i = 0; i < POP_SIZE; i++)
{
for (int j = 0; j < initParent.Length; j++)
{
initParent[j] = r.Next(1, NUM_QUEENS + 1);
}
POPULATION[i] = new Board((int[])initParent.Clone());
}
}
/// <summary>
/// crossover - Performs a random single crossover two parents
/// </summary>
/// <param name="parentX">First Parent</param>
/// <param name="parentY">Second Parent</param>
/// <returns>Child as a result of a crossover</returns>
public static Board crossover(Board parentX, Board parentY)
{
Board child;
Random r = new Random();
int crossoverPoint = r.Next(1, NUM_QUEENS - 1);
// Obtain both parts of the array from the parents, then produce a single child
int[] firstHalf = parentX.getBoard().Take(crossoverPoint).ToArray();
int[] secondHalf = parentY.getBoard().Skip(crossoverPoint).Take(NUM_QUEENS - crossoverPoint).ToArray();
int[] childArray = firstHalf.Concat(secondHalf).ToArray();
child = new Board(childArray);
return child;
}
/// <summary>
/// geneticAlg - Performs the genetic algorithm
/// </summary>
/// <returns>Child which contains a valid solution</returns>
public static Board geneticAlg()
{
Board child;
Random r = new Random();
Board[] tempPopulation = new Board[POP_SIZE];
int highestFitness = 0;
int generation = 0;
createPopulation();
while (true)
{
generation++;
// Begin creation of new generation
for (int i = 0; i < POP_SIZE; i++)
{
// Choose two parents and create a child
child = crossover(chooseParent(), chooseParent());
// Check to see if child is a solution
if (child.solved())
{
Console.Out.WriteLine("Fitness: " + child.getFitness() + " Generation: " + generation);
return child;
}
// Mutation change
if (MUTATION > r.Next(0, 100))
{
child.mutate();
}
// Check childs fitness
if (child.getFitness() > highestFitness)
{
highestFitness = child.getFitness();
}
tempPopulation[i] = child;
}
POPULATION = tempPopulation;
Console.Out.WriteLine("Fitness: " + highestFitness + " Generation: " + generation);
}
}
