public void GPNodeWriteAndRead()
{
var state = new SimpleEvolutionState();
var initializer = new GPInitializer();
state.Initializer = initializer; // Ensure that our initializer is accessible
var treeConstraints = new GPTreeConstraints();
initializer.TreeConstraints = new List <GPTreeConstraints>();
initializer.TreeConstraints.Add(treeConstraints); // Ensure that our constraints are accessible
var nodeConstraints = new GPNodeConstraints();
var rootType = new GPAtomicType("MyRootType");
treeConstraints.TreeType = rootType;
var root = new MyRootNode();
var tree = new GPTree {
Child = root
};
root.Parent = tree;
root.ArgPosition = 0; // This is the root, so there is only one position (0)
}
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 GPIndividualWriteAndRead()
{
// First we'll set up a Fitness for the Individual
var rand = new MersenneTwisterFast(0);
// Initialize some required context
var state = new SimpleEvolutionState();
var initializer = new GPInitializer();
state.Initializer = initializer; // Required for GPTree.WriteTree(...)
var constraints = new GPTreeConstraints {
Name = "TestTreeConstraints"
};
//initializer.TreeConstraints
var tree = new GPTree();
var f = new KozaFitness();
f.SetStandardizedFitness(null, float.MaxValue);
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 GPIndividual();
var ind2 = new GPIndividual(); // We'll read back into this instance
ind.Trees = new GPTree[1]; // This is the set of genes
ind.Trees[0] = new GPTree();
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
Assert.AreEqual(ind.Trees.Length, ind2.Trees.Length);
}
}
