C＃中的遗传算法？

using System;

class GeneticAlgorithm {
  // 定义基因编码
  private int[] gene;

  // 初始化种群
  public void InitializePopulation(int populationSize) {
    // 随机生成初始解
    Random random = new Random();
    for (int i = 0; i< populationSize; i++) {
      gene = new int[problemSize];
      for (int j = 0; j< problemSize; j++) {
        gene[j] = random.Next(0, 2);
      }
      // 计算适应度
      double fitness = EvaluateFitness(gene);
      // 将个体添加到种群中
      population.Add(new Individual(gene, fitness));
    }
  }

  // 选择父代个体
  public List<Individual> Selection(int eliteSize) {
    // 按适应度排序
    population.Sort();
    // 选择前eliteSize个个体作为父代
    return population.GetRange(0, eliteSize);
  }

  // 交叉
  public List<Individual> Crossover(List<Individual> parents, int offspringSize) {
    List<Individual> offspring = new List<Individual>();
    // 随机选择两个父代个体进行交叉
    Random random = new Random();
    for (int i = 0; i < offspringSize; i++) {
      int parent1Index = random.Next(0, parents.Count);
      int parent2Index = random.Next(0, parents.Count);
      int[] parent1Gene = parents[parent1Index].Gene;
      int[] parent2Gene = parents[parent2Index].Gene;
      // 交叉点
      int crossoverPoint = random.Next(0, problemSize);
      // 交换基因
      for (int j = 0; j < crossoverPoint; j++) {
        int temp = parent1Gene[j];
        parent1Gene[j] = parent2Gene[j];
        parent2Gene[j] = temp;
      }
      // 计算适应度
      double fitness1 = EvaluateFitness(parent1Gene);
      double fitness2 = EvaluateFitness(parent2Gene);
      // 将新个体添加到种群中
      offspring.Add(new Individual(parent1Gene, fitness1));
      offspring.Add(new Individual(parent2Gene, fitness2));
    }
    return offspring;
  }

  // 变异
  public void Mutation(double mutationRate) {
    // 随机选择个体进行变异
    Random random = new Random();
    for (int i = 0; i< population.Count; i++) {
      for (int j = 0; j< problemSize; j++) {
        double probability = random.NextDouble();
        if (probability < mutationRate) {
          // 翻转基因
          if (population[i].Gene[j] == 0) {
            population[i].Gene[j] = 1;
          } else {
            population[i].Gene[j] = 0;
          }
        }
      }
      // 重新计算适应度
      population[i].Fitness = EvaluateFitness(population[i].Gene);
    }
  }
}