C#遗传算法学习笔记

• 本文介绍C#遗传算法学习笔记，通过运行程序，你会发现通过不断的进化，种群的总的适应环境的能力在逐步提高。

以下代码实现了C#遗传算法一个简单的花朵进化的模拟过程。

花朵的种群数量是10，共进化了50代。通过运行程序，你会发现通过不断的进化，种群的总的适应环境的能力在逐步提高（fitness的值下降）。

C#遗传算法实现代码：

1. using System; 
2. using System.Collections.Generic; 
3. using System.Text; 
4. namespace GA 
5. { 
6. class Program 
7. { 
8. static void Main(string[] args) 
9. { 
10. World world = new World(); 
11. world.Init(); 
12. for (int i = 0; i < 50; i++) 
13. { 
14. world.Evolve(); 
15. Console.WriteLine(i); 
16. world.Show(); 
17. } 
18. } 
19. } 
20.  
21. class World 
22. { 
23. int kMaxFlowers = 11; 
24. Random Rnd = new Random(); 
25. public int[] temperature; 
26. public int[] water; 
27. public int[] sunlight; 
28. public int[] nutrient; 
29. public int[] beneficialInsect; 
30. public int[] harmfulInsect; 
31. public int currentTemperature; 
32. public int currentWater; 
33. public int currentSunlight; 
34. public int currentNutrient; 
35. public int currentBeneficialInsect; 
36. public int currentHarmfulInsect; 
37. public World() 
38. { 
39. temperature = new int[kMaxFlowers]; 
40. water = new int[kMaxFlowers]; 
41. sunlight = new int[kMaxFlowers]; 
42. nutrient = new int[kMaxFlowers]; 
43. beneficialInsect = new int[kMaxFlowers]; 
44. harmfulInsect = new int[kMaxFlowers]; 
45. } 
46. /**  
47. /// 初始化第一代花朵的基因结构 
48. ///  
49. public void Init() 
50. { 
51. for (int i = 1; i < kMaxFlowers; i++) 
52. { 
53. temperature[i] = Rnd.Next(1, 75); 
54. water[i] = Rnd.Next(1, 75); 
55. sunlight[i] = Rnd.Next(1, 75); 
56. nutrient[i] = Rnd.Next(1, 75); 
57. beneficialInsect[i] = Rnd.Next(1, 75); 
58. harmfulInsect[i] = Rnd.Next(1, 75); 
59. } 
60. currentTemperature = Rnd.Next(1, 75); 
61. currentWater = Rnd.Next(1, 75); 
62. currentSunlight = Rnd.Next(1, 75); 
63. currentNutrient = Rnd.Next(1, 75); 
64. currentBeneficialInsect = Rnd.Next(1, 75); 
65. currentHarmfulInsect = Rnd.Next(1, 75); 
66. } 
67. /**  
68. /// 越大说明花朵的适应环境的能力差，小说明适应环境的能力强 
69. ///  
70. /// name="flower"> 
71. ///  

private int Fitness(int flower)  {  int theFitness = 0;  theFitness = Math.Abs(temperature[flower] - currentTemperature);  theFitnesstheFitness = theFitness + Math.Abs(water[flower] - currentWater);  theFitnesstheFitness = theFitness + Math.Abs(sunlight[flower] -  currentSunlight);  theFitnesstheFitness = theFitness + Math.Abs(nutrient[flower] -  currentNutrient);  theFitnesstheFitness = theFitness + Math.Abs(beneficialInsect[flower] -  currentBeneficialInsect);  theFitnesstheFitness = theFitness + Math.Abs(harmfulInsect[flower] -  currentHarmfulInsect);  return (theFitness);  }  /**  /// 排除适应能力差的花朵，让适应能力强的花朵杂交繁殖，产生下一代。同时有一定的概率变异。  ///  public void Evolve()  {  int[] fitTemperature = new int[kMaxFlowers];  int[] fitWater = new int[kMaxFlowers];  int[] fitSunlight = new int[kMaxFlowers];  int[] fitNutrient = new int[kMaxFlowers];  int[] fitBeneficialInsect = new int[kMaxFlowers];  int[] fitHarmfulInsect = new int[kMaxFlowers];  int[] fitness = new int[kMaxFlowers];  int i;  int leastFit = 0;  int leastFitIndex = 1;  for (i = 1; i < kMaxFlowers; i++)  if (Fitness(i) > leastFit)  {  leastFit = Fitness(i);  leastFitIndex = i;  }  temperature[leastFitIndex] = temperature[Rnd.Next(1, 10)];  water[leastFitIndex] = water[Rnd.Next(1, 10)];  sunlight[leastFitIndex] = sunlight[Rnd.Next(1, 10)];  nutrient[leastFitIndex] = nutrient[Rnd.Next(1, 10)];  beneficialInsect[leastFitIndex] = beneficialInsect[Rnd.Next(1, 10)];  harmfulInsect[leastFitIndex] = harmfulInsect[Rnd.Next(1, 10)];  for (i = 1; i < kMaxFlowers; i++)  {  fitTemperature[i] = temperature[Rnd.Next(1, 10)];  fitWater[i] = water[Rnd.Next(1, 10)];  fitSunlight[i] = sunlight[Rnd.Next(1, 10)];  fitNutrient[i] = nutrient[Rnd.Next(1, 10)];  fitBeneficialInsect[i] = beneficialInsect[Rnd.Next(1, 10)];  fitHarmfulInsect[i] = harmfulInsect[Rnd.Next(1, 10)];  }  for (i = 1; i < kMaxFlowers; i++)  {  temperature[i] = fitTemperature[i];  water[i] = fitWater[i];  sunlight[i] = fitSunlight[i];  nutrient[i] = fitNutrient[i];  beneficialInsect[i] = fitBeneficialInsect[i];  harmfulInsect[i] = fitHarmfulInsect[i];  }  for (i = 1; i < kMaxFlowers; i++)  {  if (Rnd.Next(1, 100) == 1)  temperature[i] = Rnd.Next(1, 75);  if (Rnd.Next(1, 100) == 1)  water[i] = Rnd.Next(1, 75);  if (Rnd.Next(1, 100) == 1)  sunlight[i] = Rnd.Next(1, 75);  if (Rnd.Next(1, 100) == 1)  nutrient[i] = Rnd.Next(1, 75);  if (Rnd.Next(1, 100) == 1)  beneficialInsect[i] = Rnd.Next(1, 75);  if (Rnd.Next(1, 100) == 1)  harmfulInsect[i] = Rnd.Next(1, 75);  }  }  /**  /// 显示种群中个体对环境的适应能力，还有所有个体对环境的适应能力之和。  ///  public void Show()  {  int sum = 0;  for (int i = 1; i < kMaxFlowers; i++)  {  int fitness = Fitness(i);  sum += fitness;  Console.WriteLine("No." + i + "'s fitness is " + fitness);  }  Console.WriteLine("fitness sum is " + sum);  }  }  }

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