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我熟悉遗传编程的一般性，但我想知道在哪里可以找到一些可以向我展示实现遗传编程的细节的东西。我使用 C# 和 .NET 3.5，我想将遗传编程用于寻路之类的事情，通常只是想看看它能做什么。编辑：我可能应该澄清我在寻找什么：我对什么样的数据结构将用于存储语法树、如何执行繁殖操作等感兴趣。

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4 回答 4

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这是对帮助我学习遗传编程的C++ HelloWorld示例之一的快速重写：

using ga_vector = List<ga_struct>;

class ga_struct
{
    public ga_struct(string str, uint fitness)
    {
        Str = str;
        Fitness = fitness;
    }

    public string Str { get; set; }
    public uint Fitness { get; set; }
}

class Program
{

    private const int GA_POPSIZE = 2048;
    private const int GA_MAXITER = 16384;
    private const float GA_ELITRATE = 0.10f;
    private const float GA_MUTATIONRATE = 0.25f;
    private const float GA_MUTATION = 32767 * GA_MUTATIONRATE;
    private const string GA_TARGET = "Hello world!";

    private static readonly Random random = new Random((int)DateTime.Now.Ticks);

    static void Main(string[] args)
    {
        ga_vector popAlpha = new ga_vector();
        ga_vector popBeta = new ga_vector();

        InitPopulation(ref popAlpha, ref popBeta);
        ga_vector population = popAlpha;
        ga_vector buffer = popBeta;

        for (int i = 0; i < GA_MAXITER; i++)
        {
            CalcFitness(ref population);
            SortByFitness(ref population);
            PrintBest(ref population);

            if (population[0].Fitness == 0) break;

            Mate(ref population, ref buffer);
            Swap(ref population, ref buffer);
        }

        Console.ReadKey();
    }

    static void Swap(ref ga_vector population, ref ga_vector buffer)
    {
        var temp = population;
        population = buffer;
        buffer = temp;
    }

    static void InitPopulation(ref ga_vector population, ref ga_vector buffer)
    {
        int tsize = GA_TARGET.Length;
        for (int i = 0; i < GA_POPSIZE; i++)
        {
            var citizen = new ga_struct(string.Empty, 0);

            for (int j = 0; j < tsize; j++)
            {
                citizen.Str += Convert.ToChar(random.Next(90) + 32);
            }

            population.Add(citizen);
            buffer.Add(new ga_struct(string.Empty, 0));
        }
    }

    static void CalcFitness(ref ga_vector population)
    {
        const string target = GA_TARGET;
        int tsize = target.Length;

        for (int i = 0; i < GA_POPSIZE; i++)
        {
            uint fitness = 0;
            for (int j = 0; j < tsize; j++)
            {
                fitness += (uint) Math.Abs(population[i].Str[j] - target[j]);
            }

            population[i].Fitness = fitness;
        }
    }

    static int FitnessSort(ga_struct x, ga_struct y)
    {
        return x.Fitness.CompareTo(y.Fitness);
    }

    static void SortByFitness(ref ga_vector population)
    {
        population.Sort((x, y) => FitnessSort(x, y));
    }

    static void Elitism(ref ga_vector population, ref ga_vector buffer, int esize)
    {
        for (int i = 0; i < esize; i++)
        {
            buffer[i].Str = population[i].Str;
            buffer[i].Fitness = population[i].Fitness;
        }
    }

    static void Mutate(ref ga_struct member)
    {
        int tsize = GA_TARGET.Length;
        int ipos = random.Next(tsize);
        int delta = random.Next(90) + 32;

        var mutated = member.Str.ToCharArray();
        Convert.ToChar((member.Str[ipos] + delta)%123).ToString().CopyTo(0, mutated, ipos, 1);
        member.Str = mutated.ToString();
    }

    static void Mate(ref ga_vector population, ref ga_vector buffer)
    {
        const int esize = (int) (GA_POPSIZE*GA_ELITRATE);
        int tsize = GA_TARGET.Length, spos, i1, i2;

        Elitism(ref population, ref buffer, esize);

        for (int i = esize; i < GA_POPSIZE; i++)
        {
            i1 = random.Next(GA_POPSIZE/2);
            i2 = random.Next(GA_POPSIZE/2);
            spos = random.Next(tsize);

            buffer[i].Str = population[i1].Str.Substring(0, spos) + population[i2].Str.Substring(spos, tsize - spos);

            if (random.Next() < GA_MUTATION)
            {
                var mutated = buffer[i];
                Mutate(ref mutated);
                buffer[i] = mutated;
            }
        }
    }

    static void PrintBest(ref ga_vector gav)
    {
        Console.WriteLine("Best: " + gav[0].Str + " (" + gav[0].Fitness + ")");
    }

可能会有一些小错误，但除此之外它看起来工作正常。它也可以用 C# 的精神写得更好，但这些只是细节。:)

于 2009-06-04T09:23:13.037 回答
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Roger Alsing 的蒙娜丽莎项目就是一个很好的例子。 http://rogeralsing.com/2008/12/07/genetic-programming-evolution-of-mona-lisa/

编辑：我喜欢这个例子的原因是它相当小而且容易理解。它是掌握遗传编程概念的一种快速简便的方法。

于 2009-06-04T05:27:53.310 回答
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您可以查看适者生存：Windows 窗体的自然选择。

编辑：请参阅我刚刚发现的这个先前的 SO 问题。这几乎是重复的。抱歉，您不理解该链接（最好在问题中提及此类内容）。此外，即使答案已被接受，另一个问题仍可供更多答案/编辑。

于 2009-06-04T05:17:06.333 回答
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您可以尝试 Sean Luke 的 ECJ（Java 中的进化计算）的 C# .NET 4.0 端口：

http://branecloud.codeplex.com

它是非常灵活和强大的软件！但它也相对容易上手，因为它包含许多开箱即用的工作控制台示例（以及在转换期间开发的许多有用的单元测试）。

本

于 2011-06-01T16:49:24.930 回答