什么时候应该使用遗传算法而不是神经网络? [英] When should I use genetic algorithms as opposed to neural networks?

问题描述

是否有经验法则(或示例集)来确定何时使用遗传算法而不是神经网络(反之亦然)来解决问题?

Is there a rule of thumb (or set of examples) to determine when to use genetic algorithms as opposed to neural networks (and vice-versa) to solve a problem?

我知道在某些情况下您可以将两种方法混合使用，但是我正在寻找这两种方法之间的高级比较.

I know there are cases in which you can have both methods mixed, but I am looking for a high-level comparison between the two methods.

推荐答案

来自维基百科:

遗传算法(GA)是一种搜索技术，用于计算查找精确或近似的解决方案用于优化和搜索问题.

A genetic algorithm (GA) is a search technique used in computing to find exact or approximate solutions to optimization and search problems.

和:

神经网络是非线性统计数据建模工具.它们可用于对输入和输出之间的复杂关系进行建模，或者用于在数据中查找模式.

Neural networks are non-linear statistical data modeling tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data.

如果您有一个可以量化解决方案价值的问题，则遗传算法可以对解决方案空间进行定向搜索. (例如，找到两点之间的最短路线)

If you have a problem where you can quantify the worth of a solution, a genetic algorithm can perform a directed search of the solution space. (E.g. find the shortest route between two points)

当您有许多不同类别的项目时，神经网络可以学习"到分类以前从未见过的项目. (例如面部识别，语音识别)

When you have a number of items in different classes, a neural network can "learn" to classify items it has not "seen" before. (E.g. face recognition, voice recognition)

还必须考虑执行时间.遗传算法需要很长时间才能找到可接受的解决方案.神经网络需要很长时间才能学习"，但是几乎可以立即对新输入进行分类.

Execution times must also be considered. A genetic algorithm takes a long time to find an acceptable solution. A neural network takes a long time to "learn", but then it can almost instantly classify new inputs.

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