/// <summary>
/// Demonstrate the mutate peterb operator. An offspring will be created by randomly changing each
/// gene.
/// </summary>
public static void MutatePeterb()
{
Console.WriteLine("Mutate Perturb");
IGenerateRandom rnd = new MersenneTwisterGenerateRandom();
// Create a new population.
IPopulation pop = new BasicPopulation();
pop.GenomeFactory = new DoubleArrayGenomeFactory(5);
// 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());
var opp = new MutatePerturb(0.1);
train.AddOperation(1.0, opp);
// Create a peterb operator. Use it 1.0 (100%) of the time.
var parents = new DoubleArrayGenome[1];
parents[0] = (DoubleArrayGenome)pop.GenomeFactory.Factor();
parents[0].Population = pop;
for (int i = 1; i <= 5; i++)
{
parents[0].Data[i - 1] = i;
}
// Create an array to hold the offspring.
var offspring = new DoubleArrayGenome[1];
offspring[0] = new DoubleArrayGenome(5);
// Perform the operation
opp.PerformOperation(rnd, parents, 0, offspring, 0);
// Display the results
Console.WriteLine("Parent: " + string.Join(",", parents[0].Data));
Console.WriteLine("Offspring: " + string.Join(",", offspring[0].Data));
}
private BasicPopulation InitPopulation()
{
BasicPopulation result = new BasicPopulation(populationSize, null);
BasicSpecies defaultSpecies = new BasicSpecies()
{
Population = result
};
for (int i = 0; i < populationSize; i++)
{
IntegerArrayGenome genome = RandomGenome();
defaultSpecies.Members.Add(genome);
}
result.GenomeFactory = new IntegerArrayGenomeFactory(cities.Length);
result.Species.Add(defaultSpecies);
return(result);
}
/// <summary>
/// Construct a neat trainer with a new population.
/// </summary>
/// <param name="calculateScore">The score calculation object.</param>
/// <param name="inputCount">The input neuron count.</param>
/// <param name="outputCount">The output neuron count.</param>
/// <param name="populationSize">The population size.</param>
public NEATTraining(ICalculateScore calculateScore,
int inputCount, int outputCount,
int populationSize)
{
this.inputCount = inputCount;
this.outputCount = outputCount;
CalculateScore = new GeneticScoreAdapter(calculateScore);
Comparator = new GenomeComparator(CalculateScore);
Population = new BasicPopulation(populationSize);
// create the initial population
for (int i = 0; i < populationSize; i++)
{
Population.Add(
new NEATGenome(this, Population.AssignGenomeID(),
inputCount, outputCount));
}
Init();
}
/// <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);
}
}
public BasicPopulation CreateInitialPopulation(int populationSize, int geneCountPerChromosome)
{
try
{
var genomeFactory = new IntegerArrayGenomeFactory(geneCountPerChromosome);
var population = new BasicPopulation(populationSize, genomeFactory);
var defaultSpecies = new BasicSpecies();
for (var i = 1; i <= populationSize; i++)
{
Console.Write($"\n {i} Chomosome - ");
IntegerArrayGenome genome = CreateRandomGenome(geneCountPerChromosome);
defaultSpecies.Members.Add(genome);
}
population.Species.Add(defaultSpecies);
return(population);
}
catch (Exception e)
{
Console.WriteLine(e);
throw;
}
}
static void Main(string[] args)
{
try
{
Display("------------ Load City Coodinates");
var travellingSalesman = new TravellingSalesMan();
var cities = travellingSalesman.LoadCities(Config.CityCount, Config.MaxXCordinate, Config.MaxYCordinate);
// 1 create initial population
Display("\n------------ Create Initial Solution Population");
BasicPopulation population = travellingSalesman.CreateInitialPopulation(Config.PopulationSize, Config.CityCount);
// 2 create fitness function
Display("\n------------ Create Fitness function - sum of total diatance of cities within the chromosome - distance low --> better chormosome");
var tspFitness = new TravellingSalesManFitnessScore(cities);
// 3 create GA trainer
Display("\n------------ Create GA Trainer for iterations");
TrainEA geneticAlgorithm = travellingSalesman.CreateGA(Config.CityCount, population, tspFitness, Config.CrossOverProbabality, Config.MutationProbabality);
// 4 iterate and create off spring of new solutions
Display("\n------------ Run GA for iterations until good solutions found");
geneticAlgorithm = travellingSalesman.RunGA(geneticAlgorithm, Config.MaxNumIterationsSameSolution);
// 5 display GA results
Display("\n------------ Display Final solution after iterations");
travellingSalesman.DisplaySolution(geneticAlgorithm, cities);
Display("\n------------ Fin");
Console.ReadKey();
}
catch (Exception e)
{
Console.WriteLine(e);
throw;
}
}
