遗传算法在C#中的实现
本文关键字：实现 遗传算法 | 更新日期: 2023-09-27 18:17:05
我最近开始使用C#，目前正在尝试实现一个GA版本来解决Schwefel的函数(请参阅下面的代码(。该代码是基于我构建的一个工作处理代码。
第一代(前100个人(似乎工作得很好，但在那之后，适应度函数得到了重复值。我肯定我错过了什么，但有人知道可能是什么问题吗？
public void button21_Click(object sender, EventArgs e)
{
Population p;
// populationNum = 100;
p = new Population();
int gen = 0;
while (gen < 8000)
{
p.evolve();
}
++gen;
}
//Class Genotype
public partial class Genotype
{
public int[] genes;
public Genotype()
{
genes = new int[2];
for (int i = 0; i < genes.Length; i++)
{
Random rnd = new Random(int.Parse(Guid.NewGuid().ToString().Substring(0, 8), System.Globalization.NumberStyles.HexNumber));
//Random rnd = new Random(0);
int random = rnd.Next(256);
genes[i] = (int)random;
}
}
public void mutate()
{
//5% mutation rate
for (int i = 0; i < genes.Length; i++)
{
Random rnd = new Random(int.Parse(Guid.NewGuid().ToString().Substring(0, 8), System.Globalization.NumberStyles.HexNumber));
int random = rnd.Next(100);
if (random < 5)
{
//Random genernd = new Random();
int generandom = rnd.Next(256);
genes[i] = (int)generandom;
}
}
}
}
static Genotype crossover(Genotype a, Genotype b)
{
Genotype c = new Genotype();
for (int i = 0; i < c.genes.Length; i++)
{
//50-50 chance of selection
Random rnd = new Random(int.Parse(Guid.NewGuid().ToString().Substring(0, 8), System.Globalization.NumberStyles.HexNumber));
float random = rnd.Next(0, 1);
if (random < 0.5)
{
c.genes[i] = a.genes[i];
}
else
{
c.genes[i] = b.genes[i];
}
}
return c;
}
//Class Phenotype
public partial class Phenotype
{
double i_x;
double i_y;
public Phenotype(Genotype g)
{
i_x = g.genes[0] * 500 / 256;
i_y = g.genes[1] * 500 / 256;
}
public double evaluate()
{
double fitness = 0;
fitness -= (-1.0*i_x * Math.Sin(Math.Sqrt(Math.Abs(i_x)))) + (-1.0*i_y * Math.Sin(Math.Sqrt(Math.Abs(i_y))));
Console.WriteLine(fitness);
return fitness;
}
}
//Class Individual
public partial class Individual : IComparable<Individual>
{
public Genotype i_genotype;
public Phenotype i_phenotype;
double i_fitness;
public Individual()
{
this.i_genotype = new Genotype();
this.i_phenotype = new Phenotype(i_genotype);
this.i_fitness = 0;
}
public void evaluate()
{
i_fitness = i_phenotype.evaluate();
}
int IComparable<Individual>.CompareTo(Individual objI)
{
Individual iToCompare = (Individual)objI;
if (i_fitness < iToCompare.i_fitness)
{
return -1; //if I am less fit than iCompare return -1
}
else if (i_fitness > iToCompare.i_fitness)
{
return 1; //if I am fitter than iCompare return 1
}
return 0; // if we are equally return 0
}
}
static Individual breed(Individual a, Individual b)
{
Individual c = new Individual();
c.i_genotype = crossover(a.i_genotype, b.i_genotype);
c.i_genotype.mutate();
c.i_phenotype = new Phenotype(c.i_genotype);
return c;
}
//Class Population
public class Population
{
Individual[] pop;
int populationNum = 100;
public Population()
{
pop = new Individual[populationNum];
for (int i = 0; i < populationNum; i++)
{
this.pop[i] = new Individual();
pop[i].evaluate();
}
Array.Sort(this.pop);
}
public void evolve()
{
Individual a = select();
Individual b = select();
//breed the two selected individuals
Individual x = breed(a, b);
//place the offspring in the lowest position in the population, thus replacing the previously weakest offspring
pop[0] = x;
//evaluate the new individual (grow)
x.evaluate();
//the fitter offspring will find its way in the population ranks
Array.Sort(this.pop);
//rnd = new Random(0);
}
Individual select()
{
Random rnd = new Random(int.Parse(Guid.NewGuid().ToString().Substring(0, 8), System.Globalization.NumberStyles.HexNumber));
float random = rnd.Next(0, 1);
//skew distribution; multiplying by 99.999999 scales a number from 0-1 to 0-99, BUT NOT 100
//the sqrt of a number between 0-1 has bigger possibilities of giving us a smaller number
//if we subtract that squares number from 1 the opposite is true-> we have bigger possibilities of having a larger number
int which = (int)Math.Floor(((float)populationNum - 1e-6) * (1.0 - Math.Pow(random, random)));
return pop[which];
}
}
这是一个我认为性能良好的更新代码：
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using System.Threading;
namespace ConsoleApplication8
{
class Program
{
static Random random = new Random();
static void Main(string[] args)
{
Population p;
System.IO.StreamWriter file = new System.IO.StreamWriter("c:''test.txt");
int population = 100;
p = new Population(file, population);
int gen = 0;
while (gen <= 1000)
{
p.evolve(file);
++gen;
}
file.Close();
}
public static double GetRandomNumber(double min, double max)
{
return (random.NextDouble() * (max - min)) + min;
//return random.NextDouble() *random.Next(min,max);
}
//Class Genotype
public class Genotype
{
public int[] genes;
public Genotype()
{
this.genes = new int[2];
for (int i = 0; i < genes.Length; i++)
{
this.genes[i] = (int)GetRandomNumber(-500.0, 500.0);
}
}
public void mutate()
{
//5% mutation rate
for (int i = 0; i < genes.Length; i++)
{
if (GetRandomNumber(0.0, 100) < 5)
{
//Random genernd = new Random();
this.genes[i] = (int)GetRandomNumber(0.0, 256.0);
}
}
}
}
static Genotype crossover(Genotype a, Genotype b)
{
Genotype c = new Genotype();
for (int i = 0; i < c.genes.Length; i++)
{
//50-50 chance of selection
if (GetRandomNumber(0.0, 1) < 0.5)
{
c.genes[i] = a.genes[i];
}
else
{
c.genes[i] = b.genes[i];
}
}
return c;
}
//Class Phenotype
public class Phenotype
{
double i_x;
double i_y;
public Phenotype(Genotype g)
{
this.i_x = g.genes[0];
this.i_y = g.genes[1];
}
public double evaluate(System.IO.StreamWriter file)
{
double fitness = 0;
//fitness -= i_x + i_y;
fitness -= (i_x*Math.Sin(Math.Sqrt(Math.Abs(i_x)))) + i_y*(Math.Sin(Math.Sqrt(Math.Abs(i_y))));
file.WriteLine(fitness);
return fitness;
}
}
//Class Individual
public class Individual : IComparable<Individual>
{
public Genotype i_genotype;
public Phenotype i_phenotype;
double i_fitness;
public Individual()
{
this.i_genotype = new Genotype();
this.i_phenotype = new Phenotype(i_genotype);
this.i_fitness = 0.0;
}
public void evaluate(System.IO.StreamWriter file)
{
this.i_fitness = i_phenotype.evaluate(file);
}
int IComparable<Individual>.CompareTo(Individual objI)
{
Individual iToCompare = (Individual)objI;
if (i_fitness < iToCompare.i_fitness)
{
return -1; //if I am less fit than iCompare return -1
}
else if (i_fitness > iToCompare.i_fitness)
{
return 1; //if I am fitter than iCompare return 1
}
return 0; // if we are equally return 0
}
}
public static Individual breed(Individual a, Individual b)
{
Individual c = new Individual();
c.i_genotype = crossover(a.i_genotype, b.i_genotype);
c.i_genotype.mutate();
c.i_phenotype = new Phenotype(c.i_genotype);
return c;
}
//Class Population
public class Population
{
Individual[] pop;
//int populationNum = 100;
public Population(System.IO.StreamWriter file, int populationNum)
{
this.pop = new Individual[populationNum];
for (int i = 0; i < populationNum; i++)
{
this.pop[i] = new Individual();
this.pop[i].evaluate(file);
}
Array.Sort(pop);
}
public void evolve(System.IO.StreamWriter file)
{
Individual a = select(100);
Individual b = select(100);
//breed the two selected individuals
Individual x = breed(a, b);
//place the offspring in the lowest position in the population, thus replacing the previously weakest offspring
this.pop[0] = x;
//evaluate the new individual (grow)
x.evaluate(file);
//the fitter offspring will find its way in the population ranks
Array.Sort(pop);
}
Individual select(int popNum)
{
//skew distribution; multiplying by 99.999999 scales a number from 0-1 to 0-99, BUT NOT 100
//the sqrt of a number between 0-1 has bigger possibilities of giving us a smaller number
//if we subtract that squares number from 1 the opposite is true-> we have bigger possibilities of having a larger number
int which = (int)Math.Floor(((float)popNum - 1E-6) * (1.0 - Math.Pow(GetRandomNumber(0.0, 1.0), 2)));
return pop[which];
}
}
}
}
这是一个问题：
float random = rnd.Next(0, 1); // returns an integer from 0 to 0 as a float
// Documentation states the second argument is exclusive
尝试
float random = (float)rnd.NextDouble(); // rnd should be static, init'd once.
并且用封装阵列并允许简单的Add()、InsertAt()和RemoveAt()方法的List<Individual>替换Individual[]的所有实例。
PS。此外，常见的惯例是对所有方法和属性使用PascalCasing。
我认为最大的问题是您的select函数。
遗传算法的成功在很大程度上取决于选择正确的突变、评估和选择技术，尽管乍一看，你的选择函数似乎很容易扭曲分布，你只是根据相对位置(即Pop[0]<Pop[1](来扭曲它，但你没有考虑它们之间的差异。
在GA中，最好的个人拥有100.0的健身度和第二个拥有99.9的健身度与最好的个人有100.0的体能和第二个人拥有75.0的体能之间存在巨大差异，而你的选择函数完全忽略了这一事实。
正在发生的事情，你为什么看到重复的健身值，是因为你一次又一次地挑选大致相同的个体，使你的基因库停滞，并停滞在局部最小值(或最大值，无论你在寻找什么(。
如果你在寻找像Roullette这样的方法(http://en.wikipedia.org/wiki/Fitness_proportionate_selection)他们选择的概率是个人适合度除以总适合度的函数，根据他们的行为方式，在更多的人中分享被选中的"机会"，尽管这种方法也可能被困在当地人身上，但它比你目前拥有的方法更不容易发生，这应该会大大促进你探索搜索空间。
TL；DR-选择功能不够好，因为它过于严重地扭曲了分布，并且只考虑了相对比较。
Random.next(int min，int max(将只生成最小值和最大值之间的整数。尝试(rnd.NextDouble(生成一个介于0和1之间的随机数。这就是我现在可以帮助的：(