ga.cs

Computational Intelligence IRIS dataset Classification

using System;
using System.Collections.Generic;
using System.Text;

namespace CIProject
{
    class GA
    {
        public GA(FormMain o)
        {
            crossOverRate = 100;
            mutationRate = 100;
            chromosomeSize = 19;
            populationSize = 50;
            maxPopulationSize = (int)(crossOverRate / 100) * populationSize;
            maxPopulationSize += (int)(mutationRate / 100) * populationSize;
            population = new Population(maxPopulationSize);
            owner = o;
        }

        public void Initialize()
        {
            Random random = new Random();
            for (int i = 0; i < populationSize; i++)
            {
                population[i] = new Chromosome(chromosomeSize);
                for (int j = 0; j < chromosomeSize; j++)
                {
                    population[i][j] = random.NextDouble();
                }
            }

            for (int i = 0; i < populationSize; i++)
            {
                population.SetFitness(i, owner.CalculateFitness(population[i].ToArray()));
            }
            population.Sort(0, populationSize);
        }

        public Double[] this[int index]
        {
            get
            {
                return population[index].ToArray();
            }
        }

        public int PopulationCount
        {
            get
            {
                return populationSize;
            }
        }

        public void Iteration()
        {
            int size = (int)((crossOverRate / 100) * populationSize);
            Random random = new Random();
            int centromere = 0;
            for (int i = populationSize; i < (size + populationSize); i += 2)
            {
                Chromosome c1 = population[random.Next(0, populationSize)];
                Chromosome c2 = population[random.Next(0, populationSize)];

                centromere = random.Next(0, chromosomeSize);
                population[i] = c1.CrossOver(centromere, c2)[0];
                population.SetFitness(i, owner.CalculateFitness(population[i].ToArray()));
                population[i + 1] = c1.CrossOver(centromere, c2)[1];
                population.SetFitness(i+1, owner.CalculateFitness(population[i + 1].ToArray()));
            }
            population.Sort(0, (size + populationSize));

            size = (int)((mutationRate / 100) * populationSize);
            for (int i = populationSize; i < (size + populationSize); i++)
            {
                int index = random.Next(0, populationSize);
                Chromosome c1 = population[index];
                c1.Mutation();
                population.SetFitness(index, owner.CalculateFitness(c1.ToArray()));
            }

            population.Sort(0, (size + populationSize));
        }

        public Double[] Best
        {
            get
            {
                return this.population[0].ToArray();
            }
        }

        private FormMain owner;
        private int maxPopulationSize;
        private int populationSize;
        private int chromosomeSize;
        private float crossOverRate;
        private float mutationRate;
        private Population population;
    }
}