private static bool GenericPrimitiveGenotypeTest <T>(ArithmeticCrossover crossover, TestCase <T> testCase) where T : struct
{
var longestGenotype = testCase.Parents.Select(x => x.Length).Max();
var shortestGenotype = testCase.Parents.Select(x => x.Length).Min();
Assert.AreEqual(longestGenotype, shortestGenotype);
var expectedGenotypeLength = testCase.Parents[0].Length;
var children = crossover.Cross(testCase.Parents);
// Arithmetic Crossover must return only one child
Assert.AreEqual(1, children.Length);
var childGenotype = children[0];
Assert.NotNull(childGenotype);
var typedGenotype = childGenotype as GenericPrimitiveGenotype <T>;
Assert.NotNull(typedGenotype);
Assert.AreEqual(expectedGenotypeLength, typedGenotype.Length);
Assert.AreEqual(testCase.ExpectedValue, typedGenotype.Value);
return(true);
}
public static void Main(string[] args)
{
if (args.Length < 1)
{
Console.Error.WriteLine("You have to at least specify the data file path");
return;
}
var mutation = new UniformMutation(MutationProbability);
var selection = new KTournamentSelection(TournamentSize);
var crossover = new ArithmeticCrossover();
var fitnessFunction = new FitnessFunction(args[0]);
IGeneticAlgorithm <DecimalArrayChromosome> geneticAlgorithm;
if (args.Length == 2 && args[1].ToLower() == "gen")
{
geneticAlgorithm = new GenerationGeneticAlgorithm(mutation, selection, crossover, fitnessFunction,
IterationLimit, FitnessTerminator, PopulationSize);
}
else
{
geneticAlgorithm = new EliminationGeneticAlgorithm(mutation, selection, crossover, fitnessFunction,
IterationLimit, FitnessTerminator, PopulationSize);
}
var optimum = geneticAlgorithm.FindOptimum();
Console.WriteLine();
Console.WriteLine(optimum);
}
public void Setup()
{
_singleArithmeticCrossover = new ArithmeticCrossover(new [] { ArithmeticCrossover.EMode.Single });
}
