I used a simple genetic algorithm to optimize the signal to noise ratio of a wave that was represented as a binary string. By flipping the the bits certain ways over several million generations I was able to produce a transform that resulted in a higher signal to noise ratio of that wave. The algorithm could have also been "Simulated Annealing" but was not used in this case. At their core, genetic algorithms are simple, and this was about as simple of a use case that I have seen, so I didn't use a framework for generation creation and selection - only a random seed and the Signal-to-Noise Ratio function at hand.

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。

我曾经尝试制作一个围棋电脑播放器，完全基于基因编程。每个程序都将被视为一系列动作的评估函数。即使是在一个相当小的3x4板上，制作的程序也不是很好。

我使用Perl，并自己编写了所有代码。我今天会做不同的事情。

进化计算研究生班: 开发了TopCoder马拉松比赛49:megpartty的解决方案。我的小组正在测试不同的域表示法，以及不同的表示法如何影响ga找到正确答案的能力。我们为这个问题编写了自己的代码。

Neuroevolution and Generative and Developmental Systems, Graduate Class: Developed an Othello game board evaluator that was used in the min-max tree of a computer player. The player was set to evaluate one-deep into the game, and trained to play against a greedy computer player that considered corners of vital importance. The training player saw either 3 or 4 deep (I'll need to look at my config files to answer, and they're on a different computer). The goal of the experiment was to compare Novelty Search to traditional, fitness-based search in the Game Board Evaluation domain. Results were relatively inconclusive, unfortunately. While both the novelty search and fitness-based search methods came to a solution (showing that Novelty Search can be used in the Othello domain), it was possible to have a solution to this domain with no hidden nodes. Apparently I didn't create a sufficiently competent trainer if a linear solution was available (and it was possible to have a solution right out of the gates). I believe my implementation of Fitness-based search produced solutions more quickly than my implementation of Novelty search, this time. (this isn't always the case). Either way, I used ANJI, "Another NEAT Java Implementation" for the neural network code, with various modifications. The Othello game I wrote myself.

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。