Genetic algorithm implementation

Question

Is this the best way to implement a genetic algorithm framework? I'm just a student and wanted to know if I can improve the code that I have done. Some of the code I copied from the framework documentation:

namespace xx.xx.xx {
    class Program {


        static GrafoDistrito grafo;
        static int total;


        static void Main(string[] args) {

            using (var leitura = File.OpenText(@"C:\portugal.data")) {   

                string sTotal = leitura.ReadLine();
                total = Int32.Parse(sTotal);
                grafo = new GrafoDistrito(total);
                Random rnd = new Random();


                for (int i = 1; i <= total; i++) {

                    string line = leitura.ReadLine();               
                    string[] identificadores = line.Split(' ');
                    int id = Int32.Parse(identificadores[0]);
                    string descricao = identificadores[1];
                    Distrito distrito = new Distrito(id, descricao);
                    grafo.AdicionarVetor(distrito);
                    Console.WriteLine(identificadores[1]);
                }



                for (int j = 1; j <= total; j++) {

                    string line = leitura.ReadLine();
                    string[] identificadores = line.Split(' ');
                    int totalVis = identificadores.Length;
                    int DistAtual = Int32.Parse(identificadores[0]);



                    for (int u = 1; u < totalVis; u++) {

                        int id = Int32.Parse(identificadores[u]);
                        grafo.Connect(grafo.GetVertice(DistAtual), grafo.GetVertice(id));
                    }
                }



                var population = new Population();
                int[] arrayD = new int[total];



                for (int a = 0; a < total; a++) {
                    arrayD[a] = a + 1;
                }



                for (int u = 0; u < 100; u++) {

                    var chromosome = new Chromosome();
                    arrayD = arrayD.OrderBy(x => rnd.Next()).ToArray();



                    for (var j = 0; j < arrayD.Length; j++) {

                        chromosome.Genes.Add(new Gene(arrayD[j]));
                    }



                    chromosome.Genes.ShuffleFast();
                    population.Solutions.Add(chromosome);
                }



                var elite = new Elite(5);   //criação do operador elite
                var crossover = new Crossover(0.8);     
                var mutacao = new SwapMutate(0.02);     
                var gA = new GeneticAlgorithm(population, CalculateFitness);   



                //operators of Genetic Algorithm
                gA.Operators.Add(elite);
                gA.Operators.Add(crossover);
                gA.Operators.Add(mutacao);

                //run Genetic Algorithm
                gA.Run(Terminate);
            }
        }



        static bool Terminate(Population pop, int currentGeneration, long currentEvaluation) {

            return currentGeneration >= 400;
        }



        static double CalculateFitness(GAF.Chromosome chromossome) {

            int[] corArray = new int[chromossome.Genes.Count];
            int cor;



            foreach (Gene gene in chromossome.Genes) {

                int id = Convert.ToInt32(gene.RealValue);
                Distrito vertice = grafo.GetVertice(id);
                List<int> vizinhos = grafo.GetAresta(vertice).ToList();
                List<int> corVisivel = new List<int>();



                foreach (int a in vizinhos) {

                    int vizinhoCor = corArray[a - 1];
                    if (vizinhoCor > 0)
                        corVisivel.Add(vizinhoCor);
                }



                cor = 1;
                while (corVisivel.Contains(cor)) {
                    cor++;
                }



                corArray[id - 1] = cor;
            }



            int maxCor = 0;
            for (int i = 0; i < corArray.Length; i++) {
                if (corArray[i] > maxCor)
                    maxCor = corArray[i];         
            }



            return 1/ maxCor;   //o valor de retorno tem que ser entre 0 e 1. A melhor rota é o valor mais próximo de 1
        }



    }
}

Answer 1

You can make your code shorter and more readable with LINQ.

This:

int[] arrayD = new int[total];
for (int a = 0; a < total; a++) {
    arrayD[a] = a + 1;
}

Can be replaced with:

int[] arrayD = Enumerable.Range(1, total).ToArray();

This:

arrayD = arrayD.OrderBy(x => rnd.Next()).ToArray();
for (var j = 0; j < arrayD.Length; j++) {
    chromosome.Genes.Add(new Gene(arrayD[j]));
}

Can be replaced with:

chromosome.Genes.AddRange(arrayD.OrderBy(x => rnd.Next()));

Note that arrayD does not get shuffled each time. I'm not sure if that will affect the randomness.

This:

List<int> corVisivel = new List<int>();
foreach (int a in vizinhos) {
    int vizinhoCor = corArray[a - 1];
    if (vizinhoCor > 0)
        corVisivel.Add(vizinhoCor);
}

Can be replaced with:

List<int> corVisivel = vizinhos.Select(x => corArray[x - 1]).Where(x => x > 0).ToList();

This:

cor = 1;
while (corVisivel.Contains(cor)) {
    cor++;
}

Can be replaced with:

cor = Enumerable.Range(1, int.MaxValue).TakeWhile(x => corVisivel.Contains(x)).Count() + 1;

This:

int maxCor = 0;
for (int i = 0; i < corArray.Length; i++) {
    if (corArray[i] > maxCor)
        maxCor = corArray[i];         
}

Can be replaced with:

int maxCor = corArray.Max();

Why does Terminate() have three parameters but only use one of them? int cor; should be declared inside the foreach loop because it's not used outside of it. You can assign it on the same line that it's declared.

Answer 2

1. It is better to use a constant variable for the file path, that way you don't need to look for that line if you wish to change the file path or name.

   using (var leitura = File.OpenText(@"C:\portugal.data"))
   

2. The main function is just too long and hard to understand and maintain. Split it into a sub function. For example, the first function for reading from file AdicionarVetor() can be the code in the first loop.

3. At the beginning of Main is a parser and you converting file to objects until the line:

   var population = new Population();
   

   The use of leitura should end before that line and all the using blocks should be transferred to another functions or even a dedicated object.

   Your Main should look like this:

   static void Main(string[] args) {
       grafo = LoadGrafo(@"C:\portugal.data");
       population = BuildPopulation();
   
       var elite = new Elite(5);   //criação do operador elite
       var crossover = new Crossover(0.8);     
       var mutacao = new SwapMutate(0.02);     
       var gA = new GeneticAlgorithm(population, CalculateFitness);   
   
       //operators of Genetic Algorithm
       gA.Operators.Add(elite);
       gA.Operators.Add(crossover);
       gA.Operators.Add(mutacao);
   
       //run Genetic Algorithm
       gA.Run(Terminate);
   }
   

4. It is a bad practice to make grafo global. In this case, the program is building for a GenericAlgorithm, so this is ok. But it is up to you to extend this program. You must move grapfo and all that functionality to a dedicated class for preparing and running GenericAlgorithm, then you could manage the files and results from Main.