public void test_getValue()
{
Assert.IsTrue(0.0 == fitnessFunction.apply(
new Individual <int>(CollectionFactory.CreateQueue <int>(new[] { 0, 0, 0, 0, 0, 0, 0, 0 }))));
Assert.IsTrue(0.0 == fitnessFunction.apply(
new Individual <int>(CollectionFactory.CreateQueue <int>(new[] { 0, 1, 2, 3, 4, 5, 6, 7 }))));
Assert.IsTrue(0.0 == fitnessFunction.apply(
new Individual <int>(CollectionFactory.CreateQueue <int>(new[] { 7, 6, 5, 4, 3, 2, 1, 0 }))));
Assert.IsTrue(23.0 == fitnessFunction.apply(
new Individual <int>(CollectionFactory.CreateQueue <int>(new[] { 5, 6, 1, 3, 6, 4, 7, 7 }))));
Assert.IsTrue(28.0 == fitnessFunction.apply(
new Individual <int>(CollectionFactory.CreateQueue <int>(new[] { 0, 4, 7, 5, 2, 6, 1, 3 }))));
}
static void nQueensGeneticAlgorithmSearch()
{
System.Console.WriteLine("\nNQueensDemo GeneticAlgorithm -->");
try
{
IFitnessFunction <int> fitnessFunction = NQueensGenAlgoUtil.getFitnessFunction();
GoalTest <Individual <int> > goalTest = NQueensGenAlgoUtil.getGoalTest();
// Generate an initial population
ISet <Individual <int> > population = CollectionFactory.CreateSet <Individual <int> >();
for (int i = 0; i < 50; ++i)
{
population.Add(NQueensGenAlgoUtil.generateRandomIndividual(boardSize));
}
GeneticAlgorithm <int> ga = new GeneticAlgorithm <int>(boardSize,
NQueensGenAlgoUtil.getFiniteAlphabetForBoardOfSize(boardSize), 0.15);
// Run for a set amount of time
Individual <int> bestIndividual = ga.geneticAlgorithm(population, fitnessFunction, goalTest, 1000L);
System.Console.WriteLine("Max Time (1 second) Best Individual=\n"
+ NQueensGenAlgoUtil.getBoardForIndividual(bestIndividual));
System.Console.WriteLine("Board Size = " + boardSize);
//System.Console.WriteLine("# Board Layouts = " + (new BigDecimal(boardSize)).pow(boardSize)/*);*/
System.Console.WriteLine("Fitness = " + fitnessFunction.apply(bestIndividual));
System.Console.WriteLine("Is Goal = " + goalTest(bestIndividual));
System.Console.WriteLine("Population Size = " + ga.getPopulationSize());
System.Console.WriteLine("Iterations = " + ga.getIterations());
System.Console.WriteLine("Took = " + ga.getTimeInMilliseconds() + "ms.");
// Run till goal is achieved
bestIndividual = ga.geneticAlgorithm(population, fitnessFunction, goalTest, 0L);
System.Console.WriteLine("");
System.Console
.WriteLine("Goal Test Best Individual=\n" + NQueensGenAlgoUtil.getBoardForIndividual(bestIndividual));
System.Console.WriteLine("Board Size = " + boardSize);
//System.Console.WriteLine("# Board Layouts = " + (new BigDecimal(boardSize)).pow(boardSize)/*);*/
System.Console.WriteLine("Fitness = " + fitnessFunction.apply(bestIndividual));
System.Console.WriteLine("Is Goal = " + goalTest(bestIndividual));
System.Console.WriteLine("Population Size = " + ga.getPopulationSize());
System.Console.WriteLine("Itertions = " + ga.getIterations());
System.Console.WriteLine("Took = " + ga.getTimeInMilliseconds() + "ms.");
}
catch (Exception e)
{
throw e;
}
}
// RANDOM-SELECTION(population, FITNESS-FN)
protected virtual Individual <A> randomSelection(ICollection <Individual <A> > population, IFitnessFunction <A> fitnessFn)
{
// Default result is last individual
// (just to avoid problems with rounding errors)
Individual <A> selected = population.Get(population.Size() - 1);
// Determine all of the fitness values
double[] fValues = new double[population.Size()];
for (int i = 0; i < population.Size(); ++i)
{
fValues[i] = fitnessFn.apply(population.Get(i));
}
// Normalize the fitness values
fValues = Util.normalize(fValues);
double prob = random.NextDouble();
double totalSoFar = 0.0;
for (int i = 0; i < fValues.Length; ++i)
{
// Are at last element so assign by default
// in case there are rounding issues with the normalized values
totalSoFar += fValues[i];
if (prob <= totalSoFar)
{
selected = population.Get(i);
break;
}
}
selected.incDescendants();
return(selected);
}
public virtual Individual <A> retrieveBestIndividual(ICollection <Individual <A> > population, IFitnessFunction <A> fitnessFn)
{
Individual <A> bestIndividual = null;
double bestSoFarFValue = double.NegativeInfinity;
foreach (Individual <A> individual in population)
{
double fValue = fitnessFn.apply(individual);
if (fValue > bestSoFarFValue)
{
bestIndividual = individual;
bestSoFarFValue = fValue;
}
}
return(bestIndividual);
}