我在使用遗传算法解决课表问题。经过几次迭代后，我的适应度值开始变得相同。我尝试调整了交叉率和变异率，但没有效果。
结构：每个染色体包含多个班级。基本上，每个染色体就是一个课表。我已经实现了遗传算法。
Pseudo Code:random_population=generate_random_population(Data);while(criteria not reached){foreach(chromosome in random_population) fitness_value=calculate_fitness(chromosome)selected_population contains top 10 fittest chromosomes (selected through fitness values)random_population=perform_crossover_mutation(selected_population)}我希望每次迭代的适应度值会降低。
但是在几次迭代后，我得到了恒定值，即7。所有染色体（在一个种群中）都有相同的值！
谢谢！
编辑：不好意思，以下是代码：
主类：
/* * GA Implementation * * */ //Creating Random Population Class[][] random_population = chromoSome.generate_random_chromosomes(otherData.Total_rooms); //Playing Game with random population created above int no_of_times=0; int no_of_times_max = mainForm.total_no_of_times; while (no_of_times < no_of_times_max) //Criteria { int n = 10; //Top 10 fittest chromosomes will be selected from population Class[][] selected_chromoSomes = new Class[20][]; //fittest chromosomes array double[] fitness_values = new double[n];// fittest values array //Initializing array values for(int ij = 0; ij < n; ++ij) { fitness_values[ij] = -100000000; } //Playing Game for (int i = 0; i < random_population.Length; ++i) { //On each chromomsome applying fitness function //Storing fitness values in fitness_values array with respective chromosomes in selected chromosome array int fitness = chromoSome.fitness_fun(random_population[i], otherData,teacher_count); System.Console.Writeln("Fitness value :"+fitness); //This step is used to store fittest chromosomes for (int r = 0; r < 10; ++r) //Only storing 10 fittest chromosomes { if (fitness >= fitness_values[r]) { fitness_values[r] = fitness; selected_chromoSomes[r] = random_population[i]; r = 10; } } } //To prevent local maxima , I m initializing other selected chromosomes with random population for (int i = n; i <selected_chromoSomes.Length; ++i) { if (selected_chromoSomes[i] == null) { int rand = rnd.Next(0, random_population.Length); selected_chromoSomes[i] = random_population[rand]; } } //Applying crossover & mutation int create_n = mainForm.total_generations; //create_n tells how much new chromosomes be created from selected_chromosomes. It is 100 by default. random_population = chromoSome.apply_crossover_mutation(selected_chromoSomes, create_n, mainForm.crossover_rate);//Generating 100 new chromosomes from selected_chromosomes ++no_of_times; }染色体类：
public int fitness_fun(Class[] population,OtherData oD,int teachers_count) { //A teacher cannot teach more than 1 class at a time int score = 0; for (int t = 1; t <= teachers_count; ++t) { List<int> times = new List<int>(); List<String> days = new List<String>(); for (int i3 = 0; i3 < population.Length; ++i3) { if (population[i3].Teacher_id.Equals(t)) //Storing teacher day & times in which his/her classes are scheduled { times.Add(population[i3].TimeStart); days.Add(population[i3].Day); } } for (int i = 0; i < times.Count; ++i) { for (int j = i + 1; j < times.Count; ++j) { if (times[i] == times[j] && days[i]==days[j]) //Teacher time & day is same for 2 or more classes ! { --score; } } } } return score; //returns the fitness value } public Class[][] apply_crossover_mutation(Class[][] selected_chromosomes, int n_chromosomes, double ratio) { //ratio is by default 0.8. That means new populated is created using crossover of 80% selected chromosomes & using mutation of 20% selected chromosomes. int selected_length = selected_chromosomes.Length; //its 20 btw Class[][] all_chromosomes = new Class[n_chromosomes][];// New Population Class[][] crossover_chromosomes = new Class[Convert.ToInt32(n_chromosomes * ratio)][]; //Chromosomes generated through crossover Class[][] mutation_chromosomes = null; //Chromosomes generated through mutation if (ratio != 1) { if(ratio%2==0) mutation_chromosomes = new Class[Convert.ToInt32(n_chromosomes * (1 - ratio))][]; else { mutation_chromosomes = new Class[Convert.ToInt32(n_chromosomes * (1 - ratio))+1][]; } } //Crossover Chromosomes(One point) int index = 0; if (ratio > 0) { for (int j = 0; j < n_chromosomes * ratio; ++j) { int element1 = rnd.Next(0, selected_length); int element2 = rnd.Next(0, selected_length); int pos1 = rnd.Next(0, selected_chromosomes[0].Length); int pos2 = rnd.Next(pos1, selected_chromosomes[0].Length); Class[] chromosome = selected_chromosomes[element2]; for (int i = pos1; i < pos2; ++i) { chromosome[i] = selected_chromosomes[element1][i]; } crossover_chromosomes[index] = chromosome; ++index; } } //Mutation Chromosomes(Swap Mutation) if (ratio != 1) { index = 0; for (int j = 0; j < n_chromosomes * (1 - ratio); ++j) { int element2 = rnd.Next(0, selected_length); Class[] chromosome = selected_chromosomes[element2]; int pos1 = rnd.Next(0, selected_chromosomes[0].Length); int pos2 = rnd.Next(pos1, selected_chromosomes[0].Length); //Simply swapping values ! int t1 = chromosome[pos1].TimeStart; int t2 = chromosome[pos1].TimeEnd; String day = chromosome[pos1].Day; int room_no = chromosome[pos1].RoomNo; int teacher_id = chromosome[pos1].Teacher_id; int course_id = chromosome[pos1].Course_id; double duration = chromosome[pos1].Class_duration; Batch_Sec bs = chromosome[pos1].Bs; chromosome[pos1].TimeStart = chromosome[pos2].TimeStart; chromosome[pos1].TimeEnd = chromosome[pos2].TimeEnd; chromosome[pos1].Day = chromosome[pos2].Day; chromosome[pos1].RoomNo = chromosome[pos2].RoomNo; chromosome[pos1].Teacher_id = chromosome[pos2].Teacher_id; chromosome[pos1].Course_id = chromosome[pos2].Course_id; chromosome[pos1].Bs = chromosome[pos2].Bs; chromosome[pos1].Class_duration = chromosome[pos2].Class_duration; chromosome[pos2].TimeStart = t1; chromosome[pos2].TimeEnd = t2; chromosome[pos2].Day = day; chromosome[pos2].RoomNo = room_no; chromosome[pos2].Teacher_id = teacher_id; chromosome[pos2].Course_id = course_id; chromosome[pos2].Bs = bs; chromosome[pos2].Class_duration = duration; //Storing in mutation array mutation_chromosomes[index] = chromosome; ++index; } } //Copying crossover & mutation chromosomes in all_chromosomes int j1 = 0; for (int i = 0; i < Convert.ToInt32(n_chromosomes * ratio); ++i) { all_chromosomes[j1] = crossover_chromosomes[i]; ++j1; } for (int i = 0; i < Convert.ToInt32(n_chromosomes * (1 - ratio)); ++i) { all_chromosomes[j1] = mutation_chromosomes[i]; ++j1; } return all_chromosomes;//New Population }输出：
//First IterationFitness value: -175Fitness value: -197Fitness value: -183Fitness value: -176Fitness value: -176Fitness value: -191Fitness value: -188Fitness value: -185Fitness value: -182Fitness value: -191Fitness value: -184Fitness value: -185Fitness value: -185Fitness value: -186Fitness value: -177Fitness value: -164Fitness value: -173Fitness value: -198Fitness value: -197Fitness value: -178Fitness value: -211Fitness value: -198Fitness value: -186Fitness value: -193Fitness value: -196..........//Last IterationFitness value: -199Fitness value: -199Fitness value: -199Fitness value: -199Fitness value: -199Fitness value: -199Fitness value: -199Fitness value: -199Fitness value: -199Fitness value: -199Fitness value: -199Fitness value: -199Fitness value: -199Fitness value: -199Fitness value: -199Fitness value: -199Fitness value: -199Fitness value: -199Fitness value: -199Fitness value: -199Fitness value: -199Fitness value: -199Fitness value: -199Fitness value: -199Fitness value: -199Fitness value: -199..............Same values回答：
解决方案是，随着迭代次数的增加，我需要增加交叉和变异的频率，否则我就会陷入局部最小值！
更多信息请查看此链接：网页链接R. Logesh:在遗传算法中无法避免种群的重复。事实上，种群的重复是结果正在趋向于一个解的标志之一。如果你想要更多的种群组合，你需要增加交叉概率和变异概率。建议交叉概率高于0.8，变异概率低于0.3，以获得良好的收敛性。
