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);
}
}
public void KozaFitnessWriteAndRead()
{
var rand = new MersenneTwisterFast(0);
var f = new KozaFitness();
// Standardized fitness ranges from 0.0f inclusive to PositiveInfinity exclusive where 0.0f is ideal
// Adjusted fitness ranges from 0.0f inclusive to 1.0f exclusive, where higher is better
f.SetStandardizedFitness(null, float.MaxValue);
// Set up some random Trials just to check that they get transmitted
const int n = 10;
f.Trials = new List <double>(n);
for (var i = 0; i < n; i++)
{
f.Trials.Add(rand.NextDouble() * double.MaxValue); // in the half-open interval [0.0, double.MaxValue)
}
using (var ms = new MemoryStream())
{
var writer = new BinaryWriter(ms);
f.WriteFitness(null, writer); // Write
ms.Position = 0;
var reader = new BinaryReader(ms);
var f2 = new KozaFitness();
f2.ReadFitness(null, reader); // Read
// Compare
Assert.AreEqual(f.Value, f2.Value); // Value is same
// KozaFitness defines 'Value' as AdjustedFitness [0.0, 1.0), i.e. zero is valid but one is not
Assert.IsTrue(f.Value < 1.0f && f2.Value < 1.0f);
Assert.IsFalse(f2.IsIdeal); // Fitness is ideal
Assert.AreEqual(f2.Trials.Count, f.Trials.Count); // Number of trials is the same
for (var i = 0; i < f.Trials.Count; i++)
{
Assert.AreEqual((double)f.Trials[i], (double)f2.Trials[i]); // Trial values are all the same
}
}
}
