code世代遗传算法 [英] Code generation by genetic algorithms

问题描述

进化规划似乎是一个伟大的方式来解决许多优化问题。这个想法是很容易的，实现不使问题。

Evolutionary programming seems to be a great way to solve many optimization problems. The idea is very easy and the implementation does not make problems.

我想知道是否有任何方式的进化创造的Ruby / Python脚本程序（或任何其他语言）？

I was wondering if there is any way to evolutionarily create a program in ruby/python script (or any other language)?

这个想法很简单：

1. 创建程序人口
2. 进行遗传操作（轮盘赌轮选择或任何其他选择），从最好的项目创建新的继承程序等。
3. 在循环点2，直到方案，将满足我们的条件是找到

但仍存在一些问题：

1. 如何将染色体重新presented？例如，应该染色体的一个单元是一行code？
2. 如何将染色体会产生？如果他们将是code线，我们如何生成它们，以确保它们的语法正确，等等？

这可能产生一个程序的实施例：

Example of a program that could be generated:

创建脚本，需要N个数字作为输入并返回它们的平均值作为输出。

Create script that takes N numbers as input and returns their mean as output.

如果有任何试图建立这样的算法我会很高兴地看到任何链接/来源。

If there were any attempts to create such algorithms I'll be glad to see any links/sources.

推荐答案

如果你确定要做到这一点，你需要的遗传规划，而不是遗传算法。 GP让你进化树的结构化程序。你会怎么做是给它一堆基本的操作（而（$寄存器），读（$寄存器），增量（$寄存器），减（$寄存器），分（$结果$分子$分母），打印，progn2（这是GP代言执行两个命令顺序））。

If you are sure you want to do this, you want genetic programming, rather than a genetic algorithm. GP allows you to evolve tree-structured programs. What you would do would be to give it a bunch of primitive operations (while($register), read($register), increment($register), decrement($register), divide($result $numerator $denominator), print, progn2 (this is GP speak for "execute two commands sequentially")).

您可能会产生这样的事情：

You could produce something like this:

progn2(
  progn2(
    read($1)
    while($1
      progn2(
        while($1
          progn2( #add the input to the total
            increment($2)
            decrement($1)
          )
        )
        progn2( #increment number of values entered, read again
          increment($3)
          read($1)
        )
      )
    )
  )
  progn2( #calculate result
    divide($1 $2 $3)
    print($1)
  )
)  

您会用，因为你的健身功能，是真正的解决方案的距离。就在这里抓，那你算算，传统反正*。再有什么事情，该转换成code中（您选择的语言）。需要注意的是，当你已经有了一个潜在的无限循环在那里，你将有一段时间后切断执行（还有周围的停机问题没办法），它可能会无法正常工作。哪里哪里。还要注意，我提供的code将试图除以零。

You would use, as your fitness function, how close it is to the real solution. And therein lies the catch, that you have to calculate that traditionally anyway*. And then have something that translates that into code in (your language of choice). Note that, as you've got a potential infinite loop in there, you'll have to cut off execution after a while (there's no way around the halting problem), and it probably won't work. Shucks. Note also, that my provided code will attempt to divide by zero.

*有办法解决这个，但一般不是非常远绕过它。

*There are ways around this, but generally not terribly far around it.

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