遗传算法中有哪些不同的低级运算符？
对于我的考试，我想详细说明不同的低级 GA 运算符。但我发现不同的文本提到了不同的低级运算符。
David E. Goldberg 的搜索、优化和机器学习中的遗传算法列出了
- 优势
- 反转
- 染色体内重复
- 删除
- 易位
- 隔离
为低级运算符。
并将移民、婚姻限制和隔离列为更高层次的人口导向因素。
但其他一些文本，如 神经网络、模糊逻辑和遗传算法：S 的综合和应用Rajashekaran 和 GA Vijayalksmi 包括低级操作员的迁移。
这个低级和高级运算符之间有什么区别。
For my exam, I want to elaborate on different Low-level GA operators. But I found different texts says about different Low-level operators.
Genetic Algorithms in Search, Optimization, and Machine Learning by David E. Goldberg lists
- Dominance
- Inversrion
- Intra chromosomal duplication
- Deletion
- Translocation
- Segregation
as low-level operators.
And lists migration, marriage restriction and segregation as higher level population oriented operators.
But some other texts like Neural Networks, Fuzzy Logic and Genetic Algorithms: Synthesis and Applications by S. Rajashekaran and G.A. Vijayalksmi includes migration in low-level operators.
What is the difference between this low-level and high-level operators.
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戈德堡的低级算子改变了父母如何形成孩子，而高级算子则改变了选择如何选择父母。根据该定义，将迁移定义为低级操作员是没有意义的，Rajashekaran 和 Vijayalksmi 还给出了哪些其他示例/定义？
Goldberg's low-level operators alter how children are formed from the parents, and the high-level operators are modifying how selection chooses the parents. Defining migration as a low-level operator does not make sense under that definition, what other examples/definitions do Rajashekaran and Vijayalksmi give?