我不知道家庭作业算不算…

在我学习期间，我们推出了自己的程序来解决旅行推销员问题。

我们的想法是对几个标准进行比较(映射问题的难度，性能等)，我们还使用了其他技术，如模拟退火。

它运行得很好，但我们花了一段时间来理解如何正确地进行“复制”阶段:将手头的问题建模成适合遗传编程的东西，这对我来说是最难的部分……

这是一门有趣的课程，因为我们也涉猎了神经网络之类的知识。

我想知道是否有人在“生产”代码中使用这种编程。

当你打算粉刷你的房子时，通常很难得到一个确切的颜色组合。通常，你脑海中有一些颜色，但它不是其中一种颜色，供应商向你展示。

昨天，我的GA研究员教授提到了一个发生在德国的真实故事(对不起，我没有更多的参考资料，是的，如果有人要求我可以找到它)。这个家伙(让我们称他为配色员)曾经挨家挨户地帮助人们找到确切的颜色代码(RGB)，这将是客户心目中的衣柜。下面是他的做法:

The color guy used to carry with him a software program which used GA. He used to start with 4 different colors- each coded as a coded Chromosome (whose decoded value would be a RGB value). The consumer picks 1 of the 4 colors (Which is the closest to which he/she has in mind). The program would then assign the maximum fitness to that individual and move onto the next generation using mutation/crossover. The above steps would be repeated till the consumer had found the exact color and then color guy used to tell him the RGB combination!

通过将最大适应度分配给接近消费者想法的颜色，配色员的程序增加了收敛到消费者想法的颜色的机会。我发现它很有趣!

现在我已经得到了一个-1，如果你计划更多的-1，请说明这样做的原因!

There was an competition on codechef.com (great site by the way, monthly programming competitions) where one was supposed to solve an unsolveable sudoku (one should come as close as possible with as few wrong collumns/rows/etc as possible).What I would do, was to first generate a perfect sudoku and then override the fields, that have been given. From this pretty good basis on I used genetic programming to improve my solution.I couldn't think of a deterministic approach in this case, because the sudoku was 300x300 and search would've taken too long.

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。

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。

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.