public void CopyIndividualsForwardAndBack()
{
var state = new SimpleEvolutionState();
var job = new Job();
var ind = new FloatVectorIndividual {
Fitness = new SimpleFitness()
};
var genome = new float[1] {
1.1f
};
ind.Genome = genome;
job.Inds = new Individual[] { ind };
job.CopyIndividualsForward();
var newFit = new SimpleFitness {
Trials = new List <double>(10)
};
for (var i = 0; i < 10; i++)
{
newFit.Trials.Add(i * 100.0);
}
newFit.SetFitness(state, float.MaxValue, true);
job.NewInds[0].Fitness = newFit;
job.CopyIndividualsBack(state);
Assert.IsTrue(job.Inds[0].Fitness.Value == newFit.Value); // Fitness has been updated
for (var i = 0; i < job.Inds[0].Fitness.Trials.Count; i++)
{
Assert.AreEqual(job.Inds[0].Fitness.Trials[i], i * 100.0); // Trials have been merged into original instance
}
}
public void FloatVectorIndividualWriteAndRead()
{
// First we'll set up a Fitness for the Individual
var rand = new MersenneTwisterFast(0);
var f = new SimpleFitness();
f.SetFitness(null, float.MaxValue, true);
const int n = 10;
f.Trials = new List <double>(n);
for (var i = 0; i < n; i++)
{
f.Trials.Add(rand.NextDouble());
}
// Now we can create and initialize the Individual
var ind = new FloatVectorIndividual();
var ind2 = new FloatVectorIndividual(); // We'll read back into this instance
ind.Genome = new float[10]; // This is the set of genes
for (var i = 0; i < 10; i++)
{
ind.genome[i] = ((float)rand.NextDouble()) * float.MaxValue; // some random genes
}
ind.Fitness = f;
ind.Evaluated = true;
using (var ms = new MemoryStream())
{
var writer = new BinaryWriter(ms);
ind.WriteIndividual(null, writer);
ms.Position = 0;
var reader = new BinaryReader(ms);
ind2.Fitness = new SimpleFitness();
ind2.ReadIndividual(null, reader);
Assert.IsTrue(ind.Fitness.EquivalentTo(ind2.Fitness));
Assert.IsTrue(ind2.Fitness.IsIdeal);
Assert.IsTrue(ind.Equals(ind2)); // Genetically equivalent
for (var i = 0; i < 10; i++)
{
Assert.AreEqual(ind.genome[i], ind2.genome[i]); // check each gene
}
}
}
