基因编程实施

Question

我熟悉基因编程的一般性，但想知道我可能会发现一些东西向我展示实施遗传编程的细节。我使用c＃和.net 3.5，我想将基因编程用于诸如探路之类的事情，并且通常只想看看它可以做什么。编辑：我可能应该澄清我要寻找的内容：我对将使用哪种数据结构来存储语法树，如何执行繁殖操作以及类型的事情感兴趣。

Answer 1

这是一个快速重写之一 C ++ Helloworld 帮助我学习基因编程的示例：

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，但否则看起来还可以。另外，它可以以C＃的精神来更好地编写，但这只是细节。 :)

Answer 2

罗杰·阿尔辛（Roger Alsing）的莫娜·丽莎（Mona Lisa）项目是一个很好的例子。http://rogeralsing.com/2008/12/07/genetic-programming-evologry-evolution-of-mona-lisa/

编辑：我喜欢这个示例的原因是因为它相当小且易于理解。这是掌握基因编程概念的快速简便方法。

Answer 3

你可以看 优胜节的生存：窗户形式的自然选择.

编辑：请参阅此 以前的问题, ，我刚刚找到。这几乎是重复的。抱歉，您不了解链接（很高兴在问题中提及此类链接）。另外，即使接受了答案，另一个问题仍然开放，以获取更多答案/编辑。

Answer 4

您可以尝试此C＃.NET 4.0 Sean Luke的ECJ港口（Java中的进化计算）：

http://branecloud.codeplex.com

这是非常灵活且功能强大的软件！但这也相对容易入门，因为它包含许多开箱即用的工作台样本（以及在转换过程中开发的许多有用的单元测试）。

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