static double[] RunGeneticAlgorithm(Task task, int pop_size, int iterations, int tournament_size, double crossover_rate, double mutation_rate)
{
Population population = new Population(task.n, pop_size);
var bestScores = new double[iterations];
for (int i = 0; i < iterations; i++)
{
population.Evaluate(task);
Population new_population = new Population(pop_size);
for (int j = 0; j < pop_size; j++)
{
Individual parent1 = population.Tournament(tournament_size, task);
Individual parent2 = population.Tournament(tournament_size, task);
Individual child = parent1.Crossover(parent2, crossover_rate);
child.Mutate(mutation_rate);
new_population.individuals[j] = child;
}
population = new_population;
List <int> scores = new List <int>();
foreach (Individual el in population.individuals)
{
scores.Add(el.score);
}
bestScores[i] = (double)scores.Max();
}
return(bestScores);
}
static int RunGeneticForComparison(Task task, int pop_size, int iterations, int tournament_size, double crossover_rate, double mutation_rate)
{
geneticTime.Start();
Population population = new Population(task.n, pop_size);
var bestScores = new double[iterations];
for (int i = 0; i < iterations; i++)
{
population.Evaluate(task);
Population new_population = new Population(pop_size);
for (int j = 0; j < pop_size; j++)
{
Individual parent1 = population.Tournament(tournament_size, task);
Individual parent2 = population.Tournament(tournament_size, task);
Individual child = parent1.Crossover(parent2, crossover_rate);
child.Mutate(mutation_rate);
new_population.individuals[j] = child;
}
population = new_population;
}
geneticTime.Stop();
ts = geneticTime.Elapsed;
return(population.individuals.Max((a) => a.score));
}
