IA-ENI/GeneticAlgorithm/GeneticAlgorithm/EvolutionaryProcess.cs

using System.Collections.Generic;
using System.Linq;

namespace GeneticAlgorithm
{
    public class EvolutionaryProcess
    {
        protected List<Individual> population;
        protected int generationNb = 0;
        protected IIHM program = null;
        protected double bestFitness;
        protected string problem;

        public EvolutionaryProcess(IIHM _program, string _problem)
        {
            program = _program;
            problem = _problem;
            IndividualFactory.getInstance().Init(problem);
            population = new List<Individual>();
            for (int i = 0; i < Parameters.individualsNb; i++)
            {
                population.Add(IndividualFactory.getInstance().getIndividual(problem));
            }
        }

        public void Run()
        {
            bestFitness = Parameters.minFitness + 1;
            while (generationNb < Parameters.generationsMaxNb && bestFitness > Parameters.minFitness)
            {
                // Evaluation
                foreach (Individual ind in population)
                {
                    ind.Evaluate();
                }

                // Meilleur individu (stats)
                Individual bestInd = population.OrderBy(x => x.Fitness).FirstOrDefault();
                program.PrintBestIndividual(bestInd, generationNb);
                bestFitness = bestInd.Fitness;

                // Sélection et reproduction
                List<Individual> newGeneration = new List<Individual>();
                // Elitisme :
                newGeneration.Add(bestInd);
                for (int i = 0; i < Parameters.individualsNb - 1; i++)
                {
                    // Un ou deux parents ?
                    if (Parameters.randomGenerator.NextDouble() < Parameters.crossoverRate)
                    {
                        // Choisir parents
                        Individual father = Selection();
                        Individual mother = Selection();

                        // Reproduction
                        newGeneration.Add(IndividualFactory.getInstance().getIndividual(problem, father, mother));
                    }
                    else
                    {
                        // Choisir parent
                        Individual father = Selection();

                        // Reproduction
                        newGeneration.Add(IndividualFactory.getInstance().getIndividual(problem, father));
                    }
                }

                // Survie
                Survival(newGeneration);

                generationNb++;
            }
        }

        private void Survival(List<Individual> newGeneration)
        {
            // Remplacement
            population = newGeneration;
        }

        private Individual Selection()
        {
            // Roulette biaisée sur le rang
            int totalRanks = Parameters.individualsNb * (Parameters.individualsNb + 1) / 2;
            int rand = Parameters.randomGenerator.Next(totalRanks);

            int indIndex = 0;
            int nbParts = Parameters.individualsNb;
            int totalParts = 0;

            while(totalParts < rand) {
                indIndex++;
                totalParts += nbParts;
                nbParts --;
            }

            return population.OrderBy(x => x.Fitness).ElementAt(indIndex);
        }
    }
}