我是一个研究使用进化计算(EC)来自动修复现有程序中的错误的团队的成员。我们已经在现实世界的软件项目中成功地修复了一些真实的错误(参见本项目的主页)。

这种EC修复技术有两种应用。

The first (code and reproduction information available through the project page) evolves the abstract syntax trees parsed from existing C programs and is implemented in Ocaml using our own custom EC engine. The second (code and reproduction information available through the project page), my personal contribution to the project, evolves the x86 assembly or Java byte code compiled from programs written in a number of programming languages. This application is implemented in Clojure and also uses its own custom built EC engine.

进化计算的一个优点是技术的简单性，使得编写自己的自定义实现不太困难。有关遗传规划的一个很好的免费的介绍性文本，请参阅遗传规划的现场指南。

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。

我开发了一个基于多线程摆动的模拟机器人导航通过一组随机网格地形的食物源和矿山，并开发了一个基于遗传算法的策略，探索机器人行为的优化和机器人染色体的适者生存基因。这是使用每个迭代周期的图表和映射来完成的。

从那以后，我发展了更多的游戏行为。我最近为自己构建的一个示例应用程序是一个遗传算法，用于解决在英国寻找路线时的旅行销售人员问题，考虑到起始和目标状态，以及一个/多个连接点，延误，取消，建筑工程，高峰时间，公共罢工，考虑最快和最便宜的路线。然后为某一天的路线提供一个平衡的建议。

一般来说，我的策略是使用基于POJO的基因表示，然后为选择、突变、交叉策略和标准点应用特定的接口实现。我的适应度函数就会变得非常复杂，这是基于我需要作为启发式测量应用的策略和标准。

我还研究了将遗传算法应用于代码中的自动化测试，使用系统突变周期，其中算法理解逻辑，并尝试确定带有代码修复建议的错误报告。基本上，这是一种优化我的代码并提供改进建议的方法，以及一种自动发现新编程代码的方法。我还尝试将遗传算法应用于音乐制作和其他应用。

一般来说，我发现进化策略就像大多数元启发式/全局优化策略一样，一开始学习很慢，但随着解决方案越来越接近目标状态，只要你的适应度函数和启发式很好地对齐，在你的搜索空间内产生收敛，它们就会开始学习。

我是一个研究使用进化计算(EC)来自动修复现有程序中的错误的团队的成员。我们已经在现实世界的软件项目中成功地修复了一些真实的错误(参见本项目的主页)。

这种EC修复技术有两种应用。

The first (code and reproduction information available through the project page) evolves the abstract syntax trees parsed from existing C programs and is implemented in Ocaml using our own custom EC engine. The second (code and reproduction information available through the project page), my personal contribution to the project, evolves the x86 assembly or Java byte code compiled from programs written in a number of programming languages. This application is implemented in Clojure and also uses its own custom built EC engine.

进化计算的一个优点是技术的简单性，使得编写自己的自定义实现不太困难。有关遗传规划的一个很好的免费的介绍性文本，请参阅遗传规划的现场指南。

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.

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.