As part of my thesis I wrote a generic java framework for the multi-objective optimisation algorithm mPOEMS (Multiobjective prototype optimization with evolved improvement steps), which is a GA using evolutionary concepts. It is generic in a way that all problem-independent parts have been separated from the problem-dependent parts, and an interface is povided to use the framework with only adding the problem-dependent parts. Thus one who wants to use the algorithm does not have to begin from zero, and it facilitates work a lot.

你可以在这里找到代码。

你可以用这个算法找到的解决方案已经在科学工作中与最先进的算法SPEA-2和NSGA进行了比较，并且已经证明 算法的性能相当，甚至更好，这取决于您用来衡量性能的指标，特别是取决于您正在关注的优化问题。

你可以在这里找到它。

同样，作为我的论文和工作证明的一部分，我将这个框架应用于项目组合管理中的项目选择问题。它是关于选择对公司增加最大价值的项目，支持公司的战略或支持任何其他任意目标。例如，从特定类别中选择一定数量的项目，或最大化项目协同作用，……

我的论文将该框架应用于项目选择问题: http://www.ub.tuwien.ac.at/dipl/2008/AC05038968.pdf

之后，我在一家财富500强公司的投资组合管理部门工作，在那里他们使用了一种商业软件，该软件还将GA应用于项目选择问题/投资组合优化。

更多资源:

框架文档: http://thomaskremmel.com/mpoems/mpoems_in_java_documentation.pdf

mPOEMS演示论文: http://portal.acm.org/citation.cfm?id=1792634.1792653

实际上，只要有一点热情，每个人都可以很容易地将通用框架的代码适应任意的多目标优化问题。

没有家庭作业。

1995年，我作为专业程序员的第一份工作是为标准普尔500指数期货编写一个基于遗传算法的自动交易系统。该应用程序是用Visual Basic 3 [!我不知道我当时是怎么做的，因为VB3甚至没有课程。

The application started with a population of randomly-generated fixed-length strings (the "gene" part), each of which corresponded to a specific shape in the minute-by-minute price data of the S&P500 futures, as well as a specific order (buy or sell) and stop-loss and stop-profit amounts. Each string (or "gene") had its profit performance evaluated by a run through 3 years of historical data; whenever the specified "shape" matched the historical data, I assumed the corresponding buy or sell order and evaluated the trade's result. I added the caveat that each gene started with a fixed amount of money and could thus potentially go broke and be removed from the gene pool entirely.

在对种群的每一次评估之后，幸存者被随机杂交(通过混合来自两个亲本的片段)，一个基因被选择为亲本的可能性与它产生的利润成正比。我还添加了点突变的可能性，让事情变得有趣一点。经过几百代这样的基因，我最终得到了一个基因群，它可以把5000美元变成平均约10000美元，而且没有死亡/破碎的可能性(当然是在历史数据上)。

Unfortunately, I never got the chance to use this system live, since my boss lost close to $100,000 in less than 3 months trading the traditional way, and he lost his willingness to continue with the project. In retrospect, I think the system would have made huge profits - not because I was necessarily doing anything right, but because the population of genes that I produced happened to be biased towards buy orders (as opposed to sell orders) by about a 5:1 ratio. And as we know with our 20/20 hindsight, the market went up a bit after 1995.

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。

在学校的一次研讨会上，我们开发了一个基于音乐模式生成音乐的应用程序。该程序是在Java中构建的，输出是一个midi文件与歌曲。我们使用不同的GA方法来生成音乐。我认为这个程序可以用来探索新的组合。

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!

As part of my undergraduate CompSci degree, we were assigned the problem of finding optimal jvm flags for the Jikes research virtual machine. This was evaluated using the Dicappo benchmark suite which returns a time to the console. I wrote a distributed gentic alogirthm that switched these flags to improve the runtime of the benchmark suite, although it took days to run to compensate for hardware jitter affecting the results. The only problem was I didn't properly learn about the compiler theory (which was the intent of the assignment).

我本可以用现有的默认标志来播种初始种群，但有趣的是，算法发现了一个与O3优化级别非常相似的配置(但实际上在许多测试中更快)。

编辑:我还用Python写了我自己的遗传算法框架，只是使用popen命令来运行各种基准测试，尽管如果不是评估作业，我会看看pyEvolve。