我是一个研究使用进化计算(EC)来自动修复现有程序中的错误的团队的成员。我们已经在现实世界的软件项目中成功地修复了一些真实的错误(参见本项目的主页)。

这种EC修复技术有两种应用。

The first (code and reproduction information available through the project page) evolves the abstract syntax trees parsed from existing C programs and is implemented in Ocaml using our own custom EC engine. The second (code and reproduction information available through the project page), my personal contribution to the project, evolves the x86 assembly or Java byte code compiled from programs written in a number of programming languages. This application is implemented in Clojure and also uses its own custom built EC engine.

进化计算的一个优点是技术的简单性，使得编写自己的自定义实现不太困难。有关遗传规划的一个很好的免费的介绍性文本，请参阅遗传规划的现场指南。

除了一些常见的问题，如《旅行推销员》和Roger Alsing的《蒙娜丽莎》程序的变体，我还编写了一个进化数独求解器(这需要我自己更多的原创想法，而不仅仅是重新实现别人的想法)。解决数独游戏有更可靠的算法，但进化方法效果相当好。

在过去的几天里，在Reddit上看到这篇文章后，我一直在玩一个进化程序来寻找扑克的“冷牌”。目前还不太令人满意，但我想我可以改进。

我有自己的进化算法框架。

In 2007-9 I developed some software for reading datamatrix patterns. Often these patterns were difficult to read, being indented into scratched surfaces with all kinds of reflectance properties, fuzzy chemically etched markings and so on. I used a GA to fine tune various parameters of the vision algorithms to give the best results on a database of 300 images having known properties. Parameters were things like downsampling resolution, RANSAC parameters, amount of erosion and dilation, low pass filtering radius, and a few others. Running the optimisation over several days this produced results which were about 20% better than naive values on a test set of images unseen during the optimisation phase.

这个系统完全是从零开始编写的，我没有使用任何其他库。我并不反对使用这些东西，只要它们能提供可靠的结果，但是您必须注意许可兼容性和代码可移植性问题。

Several years ago I used ga's to optimize asr (automatic speech recognition) grammars for better recognition rates. I started with fairly simple lists of choices (where the ga was testing combinations of possible terms for each slot) and worked my way up to more open and complex grammars. Fitness was determined by measuring separation between terms/sequences under a kind of phonetic distance function. I also experimented with making weakly equivalent variations on a grammar to find one that compiled to a more compact representation (in the end I went with a direct algorithm, and it drastically increased the size of the "language" that we could use in applications).

最近，我将它们用作默认假设，以此来测试由各种算法生成的解决方案的质量。这主要涉及分类和不同类型的拟合问题(即创建一个“规则”，解释审查员对数据集所做的一组选择)。

首先，Jonathan Koza的《遗传编程》(在亚马逊上)几乎是一本关于遗传和进化算法/编程技术的书，有很多例子。我强烈建议你去看看。

As for my own use of a genetic algorithm, I used a (home grown) genetic algorithm to evolve a swarm algorithm for an object collection/destruction scenario (practical purpose could have been clearing a minefield). Here is a link to the paper. The most interesting part of what I did was the multi-staged fitness function, which was a necessity since the simple fitness functions did not provide enough information for the genetic algorithm to sufficiently differentiate between members of the population.

我曾经使用一个GA来优化内存地址的哈希函数。这些地址的页面大小为4K或8K，因此它们在地址的位模式中显示出一定的可预测性(最低有效位全为0;最初的哈希函数是“粗笨的”——它倾向于每第三个哈希桶聚集一次命中。改进后的算法具有近乎完美的分布。