使用遗传算法进行模式预测
我是遗传算法的新手。我试图预测规则出现的模式。例如，我有一组定义如下的规则。
规则 1， 规则 2， 规则 3， 规则 4， 规则 5， 规则 6，
对于给定日期，我只能使用规则 2、规则 3 和规则 6。因此，我会将这些数据表示为字符串，如下所示
0 1 1 0 0 1
，其中 1 表示使用该规则，0 表示该规则当天从未使用。
所以我会有 5 天的数据集，如下
011001， 100010, 110011, 101010, 111100，
我想在这里实现的是预测第6天的数据。我一直在阅读有关遗传算法和反向传播方法来实现这一点的内容。由于缺乏对这些概念的理解，我无法将我的问题与 GA 或 BP 联系起来。
如果有人能指出正确的方向来帮助我将我的问题与 GA 或 BP 联系起来，我将不胜感激。非常感谢任何帮助。
I am a newbie to Genetic Algorithm. I am trying to predict the pattern of occurrences of rules. For example, I have a set of rules defined as below.
Rule 1,
Rule 2,
Rule 3,
Rule 4,
Rule 5,
Rule 6,
For a given date, I could have only Rule 2, Rule 3 and Rule 6 are used. So I would represent this data as a string as stated below
0 1 1 0 0 1
where 1 denotes that the rule is used and 0 denotes that the rule never get used on that day.
So I would have set of data for 5 days as below
011001,
100010,
110011,
101010,
111100,
What I would like to achieve here is to predict the the 6th day data. I have been reading about Genetic Algorithm and Back propagation method to achieve this. I am failed to map my problem with GA or BP due to lack of understanding about those concepts.
I would appreciate if someone could point me to the right direction to help me to map my problem with either GA or BP. Any help is much appreciated.
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在这种情况下，恐怕无法预测它们！
如果上述不准确（规则出现不是纯粹随机的），您有训练集吗？有多大？你应该更多地关注这里的模式识别技术而不是 GA。
例如，循环网络似乎很适合您的问题。看看这篇论文，他们预测的是二进制时间序列而不是二进制字符串，但它已经尽可能接近了！
我想到的另一种方法可能是以类似于 本文用于财务预测。
但我猜无论哪种方式你都需要更大的训练集，并且你必须根据你的情况进行调整。
请注意：这不是一项简单的任务！
In that case there's no way to predict them I am afraid!
In case the above is not accurate (rule occurrence is not purely random), do you have a training set? How big is it? You should be looking at pattern recognition techniques here more than GAs.
For example recurrent networks seem to be a good fit for your problem. Have a look at this paper, they predict binary time series instead of binary strings but its as close as it gets!
Another approach that comes to mind could be to combine neural networks + GAs in a fashion similar to the way they're doing it on this paper here for financial prediction.
But I am guessing you need a much bigger training set either way, and you'll have to adapt it to your case.
Beware: this is not a trivial task!
遗传算法比预测更适合优化问题。然而，如果您对使用 GA 感兴趣，您可以使用它来优化神经网络的参数，该神经网络可用于预测模式。另一个有用的东西是使用线性回归的机器学习。通过线性回归，回归线可以用作预测模式的估计器。
GA's are more suited for optimization problems than prediction. If you are interested in using GA's however, you could use it to optimize the parameters for a Neural Network which could be used to predict a pattern. Another useful thing to look at is machine learning using linear regression. With linear regression a regression line can be used as an estimator for predicting the patterns.
您可以使用 GA 优化规则集，然后将优化集作为神经网络的输入进行预测，恐怕您不能使用 GA 进行预测，预测需要推理规则或格式良好的训练数据作为 NN 的输入（过去信息）。
You can optimize your rule set by using GA and then provide the optimize set as input to Neural network for prediction, I am afraid you cannot use GA for prediction, prediction required a Inference rules or a well formed training data as input to NN(past information).