我是否应该向由遗传算法训练的人工神经网络添加偏差
我有一个控制人造食草动物的人工神经网络。输入是最近植物的大小和方向、最近配偶的大小和方向以及食草动物的健康状况。输出是运动矢量（方向和幅度）。如果用遗传算法训练，是否需要使用偏差？
I have an ANN that controls an artificial herbivore. The inputs are the closest plant's magnitude and direction, the closest mate's magnitude and direction, and the herbivore's health. The outputs are a movement vector (direction and magnitude). Is it necessary to use a bias if it is being trained by a genetic algorithm?
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偏差用于将神经网络的决策边界移离原点。对于执行简单线性分类的简单感知器来说，这相当于移动分隔两个类的线。 （想想简单线性回归中的 c。
遗传算法只是搜索最佳权重的多种方法之一。它并不关心您是否有偏差，因为偏差只是它的另一个权重！
因此 使用偏差，它可以加快训练速度，并允许网络学习它可能无法学习的模式！
编辑回答您的具体问题：不，本身没有必要使用偏差，网络可以在没有它的情况下工作，但因为它很容易实现和改进您的网络 - 使用它！
The bias is used to shift the desicion boundary of the neural network away from the origin. For a simple perceptron doing simple linear classification this is equal to moving the line separating the two classes. (think of c in simple linear regression.
Genetic algorithms is just one of many ways to search for the optimal weights. It doesn't care if you have a bias or not since the bias is only another weight to it!
Therefore use a bias, it can speed up training and allows the network to learn patterns it may not be able to learn otherwise!
Edit to answer your specific question: no it isn't necessary to use a bias per se, the network can work without it, but since it's so easy to implement and improves your network- use it!
你应该使用偏差，偏差不仅可以让你解决不可线性分离的问题；但它也允许训练伪阈值，这些伪阈值是偏置神经元和其他神经元之间的互连。一般来说，它更有可能帮助你的尝试，而不是阻碍你的尝试。
You should use a bias, the bias not only allows you to solve problems that are not linearly separable; but it also allows for training of the pseudo threshold values which are interconnections between the bias neuron and the other neurons. In general, it would more likely help your attempts than hinder them.