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.

足球引爆。我建立了一个GA系统来预测每周澳式足球比赛的结果。

A few years ago I got bored of the standard work football pool, everybody was just going online and taking the picks from some pundit in the press. So, I figured it couldn't be too hard to beat a bunch of broadcast journalism majors, right? My first thought was to take the results from Massey Ratings and then reveal at the end of the season my strategy after winning fame and glory. However, for reasons I've never discovered Massey does not track AFL. The cynic in me believes it is because the outcome of each AFL game has basically become random chance, but my complaints of recent rule changes belong in a different forum.

该系统基本上考虑了进攻强度、防守强度、主场优势、每周的改进(或缺乏)以及这些方面的变化速度。这为每支球队在整个赛季中建立了一组多项式方程。可以计算给定日期的每场比赛的获胜者和分数。我们的目标是找到最接近过去所有游戏结果的系数集，并使用该集合来预测接下来几周的游戏。

在实践中，该系统将找到能够准确预测过去90%以上游戏结果的解决方案。然后，它会成功地为即将到来的一周(即不在训练集中的那一周)挑选大约60-80%的比赛。

结果是:略高于中游水平。没有巨额奖金也没有能打败维加斯的系统。不过很有趣。

我从零开始构建一切，没有使用任何框架。

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。

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。

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.

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)。