用于频率检测的遗传算法方法（在光电体积描记图中）
光电体积描记图如下所示。
基本上，您可以看到图像的亮度随着时间的变化而上升和下降。该输出可能来自测量手指血流量的脉搏血氧计。
我的问题是，是否有基于遗传算法（或通常：进化计算）的方法来计算“滴”的频率。我知道 FFT（以及 DFT）可以计算频率（或者至少为我们提供上面所示输入的频域表示）。
如果您必须使用遗传算法技术来解决这个问题，您会如何处理？ （我不是在寻找实际的解决方案，只是寻找您对 GA 设计中的表示和适应度函数的看法）。
A photoplethysomogram is shown as below.
Basically you can see the brightness of the image rise and fall as a function of time. This output could be from a pulse oximeter which measures the blood flow of your finger.
My question is this, are there genetic algorithm (or generally: evolutionary computational) based approaches to figure out the frequency of the "drops". I know FFT (well DFT) could compute the frequency (or atleast give us a frequency domain representation of the input shown above).
If you had to solve this problem using Genetic Algorithm techniques, how would you approach it? (I am not looking for actual solutions, just your ideas on how the representation and fitness functions would look in a GA design).
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这可以被认为是一个更普遍的问题的特例，即预测动力系统（时间序列），并且实际上已经有相当多的工作将遗传算法应用于此。例如，请参阅 http://www.amazon.com/Introduction-Genetic-Algorithms 中的讨论-Complex-Adaptive/dp/0262631857 第 56-61 页，或原始论文之一，作者：Norman Packard（混沌理论创始人之一）：http://www.ccsr.uiuc.edu/web/Techreports/1988-89/CCSR-89-10.pdf
-泰德
This can be thought of as a special case of a more general problem, predicting dynamical systems (timeseries), and there's actually been quite a lot of work on applying genetic algorithms to that. E.g. see the discussion in http://www.amazon.com/Introduction-Genetic-Algorithms-Complex-Adaptive/dp/0262631857 pp. 56-61, or one of the original papers, by Norman Packard (one of the founders of chaos theory): http://www.ccsr.uiuc.edu/web/Techreports/1988-89/CCSR-89-10.pdf
-Ted
我不认为 GA 最适合解决这个问题，但既然您具体询问了 GA 解决方案，这里有一些想法。对我来说，鞍点似乎更适合找到，因为它们更尖锐，而且您也可以从中获得频率。
我假设问题数据将是一个双向量，其中每个位置保存特定时间的亮度。我要求这个向量中的所有点都是从等距时隙中采样的。然后找到频率可以是找到一个偏移量和一个间隔，该偏移量和间隔最小化（或最大化）从由offset+x*interval给出的点处的问题数据获得的值的平均值。使用平均值的优点是您不需要给出最小间隔，因为一旦间隔变得太低，平均值就会下降。不幸的是，它可能会发现间隔太高，因此您还需要最大化评估适应度的点数。这会产生更棘手的多目标问题。
I don't think a GA is best suited to solve this, but since you asked specifically about a GA solution here are some thoughts. To me it seems the saddle points are better suited to be found since they are much sharper and you would get the frequency out of them as well.
I assume the problem data would be a double vector where each position holds the brightness at a certain time. I would require that all points in this vector are sampled from equidistant time slots. Then finding the frequency could be to find an offset and an interval which minimizes (or maximizes) the average of the values obtained from the problem data at the points given by offset+x*interval. The advatange of using the average is that you don't need to given a minimal interval since the average will drop once the interval becomes too low. Unfortunately, it could find an interval too high so you will also need to maximize the number of points at which the fitness is evaluated. This creates a tougher multi-objective problem.