/// <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>
/// Display the cities in the final path.
/// </summary>
/// <param name="solution">The solution to display.</param>
public void DisplaySolution(IntegerArrayGenome solution)
{
bool first = true;
int[] path = solution.Data;
foreach (int aPath in path)
{
if (!first)
Console.Write(">");
Console.Write("" + aPath);
first = false;
}
Console.WriteLine();
}
/// <summary>
/// Generate a random path through cities.
/// </summary>
/// <param name="rnd">Random number generator.</param>
/// <returns>A genome.</returns>
private IntegerArrayGenome RandomGenome(IGenerateRandom rnd)
{
var result = new IntegerArrayGenome(_cities.Length);
int[] organism = result.Data;
var taken = new bool[_cities.Length];
for (int i = 0; i < organism.Length - 1; i++)
{
int icandidate;
do
{
icandidate = rnd.NextInt(0, organism.Length);
} while (taken[icandidate]);
organism[i] = icandidate;
taken[icandidate] = true;
if (i == organism.Length - 2)
{
icandidate = 0;
while (taken[icandidate])
{
icandidate++;
}
organism[i + 1] = icandidate;
}
}
return result;
}
/// <summary>
/// Get a list of the genes that have not been taken before. This is useful
/// if you do not wish the same gene to appear more than once in a
/// genome.
/// </summary>
/// <param name="source">The pool of genes to select from.</param>
/// <param name="taken"> An array of the taken genes.</param>
/// <returns>Those genes in source that are not taken.</returns>
private static int GetNotTaken(IntegerArrayGenome source,
HashSet<int> taken)
{
foreach (int trial in source.Data)
{
if (!taken.Contains(trial))
{
taken.Add(trial);
return trial;
}
}
throw new AIFHError("Ran out of integers to select.");
}
/// <summary>
/// Construct the genome by copying another.
/// </summary>
/// <param name="other">The other genome.</param>
public IntegerArrayGenome(IntegerArrayGenome other)
{
_data = (int[]) other.Data.Clone();
}
/// <summary>
/// Construct the genome by copying another.
/// </summary>
/// <param name="other">The other genome.</param>
public IntegerArrayGenome(IntegerArrayGenome other)
{
_data = (int[])other.Data.Clone();
}
