public static Sudoku Eval(Sudoku sudoku, int populationSize, double fitnessThreshold, int generationNb)
{
//creation du chromosome
IChromosome chromosome = new SudokuPermutationsChromosome(sudoku);
var fitness = new SudokuFitness(sudoku);
var selection = new EliteSelection();
var crossover = new UniformCrossover();
var mutation = new UniformMutation();
var population = new Population(populationSize, populationSize, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = new OrTermination(new ITermination[]
{
new FitnessThresholdTermination(fitnessThreshold),
new GenerationNumberTermination(generationNb)
})
};
ga.Start();
var bestIndividual = ((ISudokuChromosome)ga.Population.BestChromosome);
var solutions = bestIndividual.GetSudokus();
return(solutions[0]);
}
static void Main(string[] args)
{
int[] initial_grid = new int[] { 0, 6, 0, 0, 5, 0, 0, 2, 0,
0, 0, 0, 3, 0, 0, 0, 9, 0,
7, 0, 0, 6, 0, 0, 0, 1, 0,
0, 0, 6, 0, 3, 0, 4, 0, 0,
0, 0, 4, 0, 7, 0, 1, 0, 0,
0, 0, 5, 0, 9, 0, 8, 0, 0,
0, 4, 0, 0, 0, 1, 0, 0, 6,
0, 3, 0, 0, 0, 8, 0, 0, 0,
0, 2, 0, 0, 4, 0, 0, 5, 0 };
//declaration du chronometre
Stopwatch stopwatch = new Stopwatch();
Noyau.Sudoku s = new Noyau.Sudoku(initial_grid);
Noyau.Sudoku s_1 = new Noyau.Sudoku();
var fitness = new SudokuFitness(s);
Console.WriteLine("Sudoku initial :");
Console.WriteLine(s.ToString());
Console.WriteLine("\n");
Console.WriteLine("******************************************************");
Console.WriteLine("\n");
Console.WriteLine("Genetic Solver");
GeneticSolver gs = new GeneticSolver();
//chrono start
stopwatch.Start();
s_1 = gs.Solve(s);
//chrono stop
stopwatch.Stop();
Console.WriteLine(s_1.ToString());
//fonction pour evaluer si un sudoku est bon : objectif 0
Console.WriteLine("Fitness : ");
Console.WriteLine(fitness.Evaluate(s_1));
//instruction durée d'exe
Console.WriteLine("Durée d'exécution: {0} secondes", stopwatch.Elapsed.TotalSeconds);
stopwatch.Reset();
}
public static Sudoku Eval(Sudoku sudoku, int populationSize, double fitnessThreshold, int generationNb)
{
//creation du chromosome
IChromosome chromosome = new SudokuPermutationsChromosome(sudoku);
//variable qui indique l'erreure
var fitness = new SudokuFitness(sudoku);
//choix de la selection : ici elite
var selection = new EliteSelection();
//var selection = new RouletteWheelSelection();
//var selection = new SelectionException();
//var selection = new TournamentSelection();
//Choix du crossover : ici uniform
var crossover = new UniformCrossover();
//choix de la mutation : ici uniform
var mutation = new UniformMutation();
//creation de la population
var population = new Population(populationSize, populationSize, chromosome);
//création de l'algo génétique
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
//pour mettre fin a l'exec si on atteint le threshold ou le nombre de rep fixé
Termination = new OrTermination(new ITermination[]
{
new FitnessThresholdTermination(fitnessThreshold),
new GenerationNumberTermination(generationNb)
})
};
ga.Start();
//recupération de la meilleure solution
var bestIndividual = ((ISudokuChromosome)ga.Population.BestChromosome);
var solutions = bestIndividual.GetSudokus();
return(solutions[0]);
}
