/// <summary>
/// Create an initial population.
/// </summary>
/// <param name="rnd">Random number generator.</param>
/// <param name="codec">The codec, the type of network to use.</param>
/// <returns>The population.</returns>
public static IPopulation InitPopulation(IGenerateRandom rnd, RBFNetworkGenomeCODEC codec)
{
// Create a RBF network to get the length.
var network = new RBFNetwork(codec.InputCount, codec.RbfCount, codec.OutputCount);
int size = network.LongTermMemory.Length;
// Create a new population, use a single species.
IPopulation result = new BasicPopulation(PopulationSize, new DoubleArrayGenomeFactory(size));
var defaultSpecies = new BasicSpecies {Population = result};
result.Species.Add(defaultSpecies);
// Create a new population of random networks.
for (int i = 0; i < PopulationSize; i++)
{
var genome = new DoubleArrayGenome(size);
network.Reset(rnd);
Array.Copy(network.LongTermMemory, 0, genome.Data, 0, size);
defaultSpecies.Add(genome);
}
// Set the genome factory to use the double array genome.
result.GenomeFactory = new DoubleArrayGenomeFactory(size);
return result;
}
/// <summary>
/// Demonstrate the crossover splice operator. Two offspring will be created by swapping three
/// segments of the parents (two cut points). Some genes may repeat.
/// </summary>
public static void Splice()
{
Console.WriteLine("Crossover Splice");
// Create a random number generator
IGenerateRandom rnd = new MersenneTwisterGenerateRandom();
// Create a new population.
IPopulation pop = new BasicPopulation();
pop.GenomeFactory = new IntegerArrayGenomeFactory(10);
// Create a trainer with a very simple score function. We do not care
// about the calculation of the score, as they will never be calculated.
IEvolutionaryAlgorithm train = new BasicEA(pop, new NullScore());
// Create a splice operator, length = 5. Use it 1.0 (100%) of the time.
var opp = new Splice(5);
train.AddOperation(1.0, opp);
// Create two parents, the genes are set to 1,2,3,4,5,7,8,9,10
// and 10,9,8,7,6,5,4,3,2,1.
var parents = new IntegerArrayGenome[2];
parents[0] = (IntegerArrayGenome) pop.GenomeFactory.Factor();
parents[1] = (IntegerArrayGenome) pop.GenomeFactory.Factor();
for (int i = 1; i <= 10; i++)
{
parents[0].Data[i - 1] = i;
parents[1].Data[i - 1] = 11 - i;
}
// Create an array to hold the offspring.
var offspring = new IntegerArrayGenome[2];
// Perform the operation
opp.PerformOperation(rnd, parents, 0, offspring, 0);
// Display the results
Console.WriteLine("Parent 1: " + string.Join(",", parents[0].Data));
Console.WriteLine("Parent 2: " + string.Join(",", parents[1].Data));
Console.WriteLine("Offspring 1: " + string.Join(",", offspring[0].Data));
Console.WriteLine("Offspring 2: " + string.Join(",",
offspring[1].Data));
}
/// <summary>
/// The entry point for this example. If you would like to make this example
/// stand alone, then add to its own project and rename to Main.
/// </summary>
/// <param name="args">Not used.</param>
public static void ExampleMain(string[] args)
{
// Create a new population.
IPopulation pop = new BasicPopulation();
ISpecies species = pop.CreateSpecies();
// Create 1000 genomes, assign the score to be the index number.
for (int i = 0; i < 1000; i++)
{
IGenome genome = new IntegerArrayGenome(1);
genome.Score = i;
genome.AdjustedScore = i;
pop.Species[0].Add(genome);
}
IGenerateRandom rnd = new MersenneTwisterGenerateRandom();
// Create a trainer with a very simple score function. We do not care
// about the calculation of the score, as they will never be calculated.
// We only care that we are maximizing.
IEvolutionaryAlgorithm train = new BasicEA(pop, new NullScore());
// Perform the test for round counts between 1 and 10.
for (int roundCount = 1; roundCount <= 10; roundCount++)
{
var selection = new TournamentSelection(train, roundCount);
int sum = 0;
int count = 0;
for (int i = 0; i < 100000; i++)
{
int genomeID = selection.PerformSelection(rnd, species);
IGenome genome = species.Members[genomeID];
sum += (int) genome.AdjustedScore;
count++;
}
sum /= count;
Console.WriteLine("Rounds: " + roundCount + ", Avg Score: " + sum);
}
}
/// <summary>
/// Create an initial random population of random paths through the cities.
/// </summary>
/// <param name="rnd">The random population.</param>
/// <returns>The population</returns>
private IPopulation InitPopulation(IGenerateRandom rnd)
{
IPopulation result = new BasicPopulation(PopulationSize, null);
var defaultSpecies = new BasicSpecies();
defaultSpecies.Population = result;
for (int i = 0; i < PopulationSize; i++)
{
IntegerArrayGenome genome = RandomGenome(rnd);
defaultSpecies.Add(genome);
}
result.GenomeFactory = new IntegerArrayGenomeFactory(_cities.Length);
result.Species.Add(defaultSpecies);
return result;
}
/**
* Create the initial random population.
*
* @return The population.
*/
private IPopulation InitPopulation() {
IPopulation result = new BasicPopulation(PlantUniverse.PopulationSize, null);
BasicSpecies defaultSpecies = new BasicSpecies();
defaultSpecies.Population = result;
for (int i = 0; i < PlantUniverse.PopulationSize; i++) {
DoubleArrayGenome genome = RandomGenome();
defaultSpecies.Add(genome);
}
result.GenomeFactory = new DoubleArrayGenomeFactory(PlantUniverse.GenomeSize);
result.Species.Add(defaultSpecies);
return result;
}
/// <summary>
/// Create an initial random population.
/// </summary>
/// <param name="rnd">A random number generator.</param>
/// <param name="eval">The expression evaluator.</param>
/// <returns>The new population.</returns>
private IPopulation InitPopulation(IGenerateRandom rnd, EvaluateExpression eval)
{
IPopulation result = new BasicPopulation(PopulationSize, null);
var defaultSpecies = new BasicSpecies();
defaultSpecies.Population = result;
for (int i = 0; i < PopulationSize; i++)
{
TreeGenome genome = RandomGenome(rnd, eval);
defaultSpecies.Add(genome);
}
result.GenomeFactory = new TreeGenomeFactory(eval);
result.Species.Add(defaultSpecies);
return result;
}
