在差分进化算法中交叉的重要性是什么? [英] what is the importance of crossing over in Differential Evolution Algorithm?

问题描述

在差分进化算法中用于优化问题. 涉及三个进化过程，即突变穿越和选择

In Differential Evolution Algorithm for optimization problems. There are three evolutionary processes involved, that is mutation crossing over and selection

我只是一个初学者，但我尝试过删除交叉过程，并且与原始算法没有明显差异.

I am just a beginner but I have tried removing the crossing over process and there is no significant difference result from the original algorithm.

那么在差分进化算法中交叉的重要性是什么?

So what is the importance of crossing over in Differential Evolution Algorithm?

推荐答案

如果您不使用交叉，则可能是您的算法只是探索问题搜索空间而没有利用它.通常，如果进化算法能够在勘探和开发速率之间取得良好的平衡，那么该算法便会成功.

If you don't use crossover may be your algorithm just explore the problem search space and doesn't exploit it. In general an evolutionary algorithm succeeds if it makes good balance between exploration and exploitation rates.

例如DE/rand/1/Either-Or是DE的变体，它消除了交叉算子，但使用了有效的变异算子.根据

For example DE/rand/1/Either-Or is a variant of DE which eliminates crossover operator but uses effective mutation operator. According to Differential Evolution: A Survey of the State-of-the-Art, in this Algorithm, trial vectors that are pure mutants occur with a probability pF and those that are pure recombinants occur with a probability 1 − pF. This variant is shown to yield competitive results against classical DE-variants rand/1/bin and target-to-best/1/bin (Main Reference).

X(i,G)是G世代的第i个目标(父)向量，U(i,G)是其对应的试验向量，F是差异向量比例因子和k = 0.5*(F + 1)[in the original paper]. 在该方案中，不使用交叉，但变异足以与原始DE算法进行比较.

X(i,G) is the i-th target (parent) vector of Generation G, U(i,G) is it's corresponding trial vector,F is difference vector scale factor and k = 0.5*(F + 1)[in the original paper].
In this scheme crossover isn't used but mutation is effective enough to compare with original DE algorithm.

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