static void Main(string[] args)
{
var fitness = new TspFitness(m_numberOfCities);
var chromosome = new TspChromosome(m_numberOfCities);
// This operators are classic genetic algorithm operators that lead to a good solution on TSP,
// but you can try others combinations and see what result you get.
var crossover = new GeneticSharp.Domain.Crossovers.OrderedCrossover();
var mutation = new GeneticSharp.Domain.Mutations.ReverseSequenceMutation();
var selection = new GeneticSharp.Domain.Selections.RouletteWheelSelection();
var population = new GeneticSharp.Domain.Populations.Population(500, 1000, chromosome);
m_ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
m_ga.Termination = new GeneticSharp.Domain.Terminations.TimeEvolvingTermination(System.TimeSpan.FromHours(1));
// The fitness evaluation of whole population will be running on parallel.
m_ga.TaskExecutor = new ParallelTaskExecutor
{
MinThreads = 100,
MaxThreads = 200
};
// Every time a generation ends, we log the best solution.
m_ga.GenerationRan += delegate
{
var distance = ((TspChromosome)m_ga.BestChromosome).Distance;
Console.WriteLine($"Generation: {m_ga.GenerationsNumber} - Distance: ${distance}");
};
// Starts the genetic algorithm in a separate thread.
m_gaThread = new Thread(() => m_ga.Start());
m_gaThread.Start();
Console.ReadLine();
// When the script is destroyed we stop the genetic algorithm and abort its thread too.
m_ga.Stop();
m_gaThread.Abort();
}
public BuildGeneticAlgorithm(int countGenes, int minSizePopulation = 2, int maxSizePopulation = 100)
{
_chromosome = new BinaryChromosome(countGenes);
_population = new GeneticSharp.Domain.Populations.Population(minSizePopulation, maxSizePopulation, _chromosome);
}
static void Main(string[] args)
{
// var chromosome = new E2Chromosome(5, 5);
// Console.WriteLine(chromosome.ToString());
//
// int n = 0;
// foreach (var cutTile in E2Chromosome.CutTiles(chromosome))
// {
// Console.Write(cutTile + " ");
// if ( ++n % 5 == 0)Console.WriteLine();
// }
//
// Console.ReadLine();
// Setup problem
E2Chromosome.Width = 5;
E2Chromosome.Height = 5;
E2Chromosome.TileCount = E2Chromosome.Width * E2Chromosome.Height;
E2Chromosome.Tiles = new string[]
{
"ABDC", "DEBF", "BGGE", "GCFF", "FEED",
"CCBF", "BFCC", "CEGG", "GFAH", "ADDC",
"FFAC", "ACHE", "HGBC", "BHAF", "ACHG",
"ECFC", "FEDB", "DCGB", "GFCA", "CGFD",
"ECBE", "BBGC", "GBCH", "CAHD", "HDCE"
}.Select(t => new Tile(t));
// Create chromosome and fitness
var chromosome = new E2Chromosome();
var fitness = new E2Fitness();
// This operators are classic genetic algorithm operators that lead to a good solution on TSP,
// but you can try others combinations and see what result you get.
var crossover = new GeneticSharp.Domain.Crossovers.OnePointCrossover();
var mutation = new GeneticSharp.Domain.Mutations.PartialShuffleMutation();
var selection = new GeneticSharp.Domain.Selections.RouletteWheelSelection();
var population = new GeneticSharp.Domain.Populations.Population(5000, 10000, chromosome);
ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.MutationProbability = 0.1f;
ga.Termination = new GeneticSharp.Domain.Terminations.FitnessThresholdTermination(1.0);
// The fitness evaluation of whole population will be running on parallel.
ga.TaskExecutor = new ParallelTaskExecutor
{
MinThreads = 100,
MaxThreads = 200
};
// Every time a generation ends, we log the best solution.
int bestMatches = 0;
DateTime startTime = DateTime.Now;
ga.GenerationRan += delegate
{
var best = ((E2Chromosome)ga.BestChromosome);
var Matches = best.Matches;
var Fitness = best.FitnessValue;
if (Matches > bestMatches || ga.GenerationsNumber % 250 == 0)
{
Console.WriteLine(
$"{DateTime.Now.Subtract(startTime).TotalSeconds}s Generation: {ga.GenerationsNumber} - Matches: {Matches} - Fitness: {Fitness}");
if (Matches > bestMatches)
{
Console.WriteLine(((E2Chromosome)ga.BestChromosome).ToStringIncludingTileMatches(true));
}
bestMatches = Matches;
}
};
// Starts the genetic algorithm in a separate thread.
gaThread = new Thread(() => ga.Start());
gaThread.Start();
Console.ReadLine();
// When the script is destroyed we stop the genetic algorithm and abort its thread too.
ga.Stop();
gaThread.Abort();
}
