我和一个同事正在研究一种解决方案，使用我们公司要求的各种标准将货物装载到卡车上。我一直在研究遗传算法的解决方案，而他正在使用具有激进修剪的分支和绑定。我们仍在实施这个解决方案的过程中，但到目前为止，我们已经取得了良好的结果。

在大学期间，我们使用NERO(神经网络和遗传算法的结合)来教游戏中的机器人做出智能决策。非常酷。

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.

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。

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。

我构建了一个简单的GA，用于在音乐播放时从频谱中提取有用的模式。输出用于驱动winamp插件中的图形效果。

输入:一些FFT帧(想象一个二维浮点数组) 输出:单个浮点值(输入的加权和)，阈值为0.0或1.0 基因:输入权重 适应度函数:占空比、脉宽、BPM在合理范围内的组合。

我将一些ga调整到频谱的不同部分以及不同的BPM限制，所以它们不会趋向于收敛到相同的模式。来自每个种群的前4个的输出被发送到渲染引擎。

一个有趣的副作用是，整个人群的平均健康状况是音乐变化的一个很好的指标，尽管通常需要4-5秒才能发现。