从进化计算动物身上学到的教训
Felipe Campelo1, Claus Aranha2
1Aston University, College of Engineering and Physical Sciences. f.campelo@aston.ac.uk.
Artificial life
|July 11, 2023
概括
灵感来自大自然的元启发学,现在受到许多以隐喻为中心的算法的影响. 这种趋势阻碍了科学进步,因为它创造了多余的变体,并掩盖了可通用的优化原则.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 优化优化 优化优化
背景情况:
- 从历史上看,元启发学从进化和群体行为等自然系统中汲取灵感.
- 近几十年来,以各种各样的,有时是不寻常的现象为灵感的以隐喻为中心的算法越来越多.
研究的目的:
- 分析隐喻驱动的元启发学趋势.
- 讨论这种趋势对科学进步的负面影响.
- 提出一种更加平衡的方法来发展元启发学.
主要方法:
- 文学评论和对元启发算法趋势的概念分析.
- 讨论命名惯例和算法变体的影响.
- 探索观察到的趋势的潜在原因和解决方案.
主要成果:
- 该领域充满了众多,微妙不同的算法,缺乏明显的贡献.
- 这种扩散阻碍了对生物系统的理解和可概括的优化原则的开发.
- 在一些基于隐喻的方法中缺乏科学严谨性.
结论:
- 在元启发学中过度使用隐喻已经变得反作用.
- 需要转向更大的科学健全性,减少依赖肤浅的灵感.
- 鼓励研究侧重于基本原则,而不是新的隐喻,将有利于该领域.
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