public DemoPlayerInputStrategy()
{
var population = new Population (6, 6, new DemoPlayerChromosome ());
var fitness = new DemoPlayerFitness ();
var selection = new EliteSelection ();
var crossover = new OnePointCrossover (0);
var mutation = new UniformMutation (true);
m_ga = new GeneticAlgorithm (
population,
fitness,
selection,
crossover,
mutation);
m_ga.MutationProbability = 0.5f;
m_ga.GenerationRan += (sender, e) => {
m_bestChromossome = m_ga.BestChromosome as DemoPlayerChromosome;
if(m_bestChromossome.AvoidAliensProjectilesProbability > 0.9f) {
HorizontalDirection = 1f;
}
};
m_ga.Start ();
SHThread.PingPong (.01f, 0, 1, (t) => {
m_ga.Termination = new GenerationNumberTermination (m_ga.GenerationsNumber + 1);
m_ga.Resume();
return true;
});
}
public void OnePointCrossoverTest()
{
IChromosome<int> a = new DigitalChromosome().GenerateFromArray(new int[] { 1, 2, 3, 4 });
IChromosome<int> b = new DigitalChromosome().GenerateFromArray(new int[] { 4, 3, 2, 1 });
OnePointCrossover<int> cross = new OnePointCrossover<int>(2);
IChromosome<int>[] res = cross.Crossover(a, b);
IChromosome<int>[] exp = new IChromosome<int>[2] { new DigitalChromosome().GenerateFromArray(new int[] { 1, 2, 2, 1 }), new DigitalChromosome().GenerateFromArray(new int[] { 4, 3, 3, 4 }) };
CollectionAssert.AreEqual(res, exp);
}
