public Population(int size, IFitnessFunction fitnessFunction, IReproduction reproductionFunction, INodeMutator mutator, ISelection selection)
{
this.populationSize = size;
this.fitnessFunction = fitnessFunction;
this.reproductionFunction = reproductionFunction;
this.mutator = mutator;
this.selector = selection;
this.fitnessFunction.Initialise();
// Create the initial population
for (int i = 0; i < size; i++)
{
try
{
NodeContext zeroContext = new NodeContext();
zeroContext.AvailableCollections = fitnessFunction.GetCollections();
zeroContext.AvailableInputs = fitnessFunction.GetInputs();
INode candidateNode = NodeFactory.GenerateNode(zeroContext);
// Make sure we have a decent candidate (i.e. not too large)
double fitness = this.fitnessFunction.CalculateFitness(candidateNode);
if (fitness == Double.MaxValue) continue;
this.population.Add(NodeFactory.GenerateNode(zeroContext));
}
catch (StackOverflowException)
{
}
}
}
public object Clone()
{
NodeContext result = new NodeContext();
result.DepthLevel = this.DepthLevel;
result.AvailableCollections = new List<int>(this.AvailableCollections);
result.AvailableInputs = new List<int>(this.AvailableInputs);
return result;
}
public static INode GenerateNode(NodeContext context)
{
INode result = null;
if (context.DepthLevel > 20)
{
result = new ConstantNode(context.GenerateNewLevel());
return result;
}
int probability = 10;
while (result == null)
{
if (RandomUtil.Random.Next(100) < probability)
{
result = new AddNode(context.GenerateNewLevel());
}
else if (RandomUtil.Random.Next(100) < probability)
{
result = new SubtractNode(context.GenerateNewLevel());
}
else if (RandomUtil.Random.Next(100) < probability)
{
result = new MultiplyNode(context.GenerateNewLevel());
}
else if (RandomUtil.Random.Next(100) < probability)
{
result = new DivideNode(context.GenerateNewLevel());
}
else if (RandomUtil.Random.Next(100) < probability)
{
result = new InputSizeNode(context.GenerateNewLevel());
}
else if (RandomUtil.Random.Next(100) < probability)
{
result = new CollectionSizeNode(context.GenerateNewLevel());
}
else if (RandomUtil.Random.Next(100) < probability && context.AvailableInputs.Count > 0)
{
result = new InputVariableNode(context.GenerateNewLevel());
}
else if (RandomUtil.Random.Next(100) < probability && context.AvailableCollections.Count > 0)
{
result = new SumNode(context.GenerateNewLevel());
}
}
//NodeFactory.SimplifyNode(ref result);
return result;
}
/// <summary>
/// Generate a new population based on the fitness of the previous population's
/// individuals
/// </summary>
public void ProcessGeneration()
{
NodeContext zeroContext = new NodeContext();
zeroContext.AvailableCollections = fitnessFunction.GetCollections();
zeroContext.AvailableInputs = fitnessFunction.GetInputs();
// Create a new set of tests
fitnessFunction.Initialise();
Console.WriteLine("Calculate");
// Get the fitness measure for each individual
var fitnesses = new ThreadSafeDictionary<int, double>();
for (int i = 0; i < this.population.Count; ++i)
{
fitnesses[i] = this.fitnessFunction.CalculateFitness(this.population[i]);
fitnesses[i] = Math.Abs(fitnesses[i]);
}
//var loopResult = Parallel.For(0, this.population.Count, delegate(int i)
//{
// fitnesses[i] = Math.Abs(this.fitnessFunction.CalculateFitness(this.population[i]));
// //fitnesses[i] = Math.Abs(fitnesses[i]);
//});
//if (!loopResult.IsCompleted)
//{
// Console.WriteLine("Loop failed");
//}
this.selector.Initialise(this.population, fitnesses);
var bestIndividuals = from k in fitnesses.Keys orderby fitnesses[k] ascending select this.population[k];
INode best = bestIndividuals.FirstOrDefault();
this.BestIndividual = best;
// Update the best fitness
this.BestFitness = fitnesses.Values.Min();
this.AverageFitness = fitnesses.Values.Sum(f => f < Double.MaxValue ? f : 0)/fitnesses.Values.Count(f => f < Double.MaxValue);
Console.WriteLine("Best fitness: {0} {1}", fitnesses.Values.Min(), best);
this.PrintAverageTreeSize();
List<INode> newPopulation = new List<INode>();
// Always keep the best 10%
newPopulation.AddRange(bestIndividuals.Take(this.populationSize / 10));
while (newPopulation.Count < this.populationSize)
{
INode parentA = this.selector.SelectNode();
INode parentB = this.selector.SelectNode();
INode child = this.reproductionFunction.Reproduce(parentA, parentB);
//INode child = (INode) this.selector.SelectNode().Clone();
this.mutator.Mutate(ref child, zeroContext);
newPopulation.Add(child);
}
// Replace the old population);
this.population = newPopulation;
}
