遗传算法-什么是稳态选择? [英] Genetic Algorithm - what is steady state selection?

问题描述

我正在做有关遗传算法的最后一年的项目，特别是Dawkins Weasel类型的.我已经完成轮盘赌选择和锦标赛选择，但仍然要进行稳态选择，但是我不确定它到底是什么，而且我在网上找到的参考文献都还很模糊.

I'm doing a final year project on genetic algorithms - specifically of the Dawkins Weasel type. I've done roulette selection and tournament selection, still to do steady state selection, but I'm not sure exactly what it is and references I find online are all pretty vague.

有人知道应该如何实施吗?任何指针都很棒.

Does anyone know how it should be implemented? Any pointers would be great.

非常感谢.

推荐答案

通常，遗传算法的运行分为几代-您选择和繁殖过程中的每一代都将取代全部(或至少大部分)种群.在稳态遗传算法中，一次只能替换几个人.

Typically, the run of a genetic algorithm is divided into generations - each generation your selection and reproduction process replaces all (or at least most) of the population. In a steady state genetic algorithm you only replace a few individuals at a time.

使用标准的选择技术挑选父母来生产这几个后代.然后随机选择相同数量的个体，将其杀死，然后将其替换为后代(您可以选择不适合的个体死亡，但这可能会在一个不重要的问题上消除种群多样性).

Use a standard selection technique to pick parents to produce these few offspring. Then randomly select the same number of individuals, kill them off, and replace them with the offspring (you could select unfit individuals for death, but that may wipe out population diversity in a non-trivial problem).

您每个人只能评估一次适应度-评估适应度后，保存并在以后再使用该数字.提示:创建新个体时，将其标记为未评估，然后在首次需要时对其进行评估(这样，如果创建了一个个体，然后在使用前随机选择将其死亡，则无需浪费时间评估其适应性.)

You should only evaluate fitness once per individual - after you evaluate the fitness, save it and then reuse that number in the future. Protip: when a new individual is created, flag it as being unevaluated, and then evaluate it the first time it's needed (this way, if an individual is created and then randomly selected for death before being used, you don't consume time evaluating its fitness).

基本实现应该非常简单，但是您可以查看元启发式方法的本质(第45-46页，免费提供电子书).

A basic implementation should be fairly simple, but you can check out Essentials of Metaheuristics (pages 45-46, ebook available free).

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