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摘要翻译：
尽管研究人员经常评论受自然启发的元启发式(NIM)的日益流行，但直接支持NIM比其他优化技术日益突出的说法的数据很少。这项研究提供了证据，表明NIM的使用不仅在增长，而且在与学术研究活动（出版频率）和商业活动（专利频率）有关的几个重要指标上，确实似乎已经超过了数学优化技术(MOT)。在这些发现的激励下，本文讨论了这种日益流行的一些可能的起源。我回顾了对NIM受欢迎程度的不同解释，并讨论了为什么这些论点中的一些仍然不能令人满意。我认为，一个令人信服和全面的解释应该直接说明大多数NIM成功的方式实际上是如何实现的，例如，通过对不同问题环境的杂交和定制。通过从问题生命周期的角度出发，本文提供了一个新的观点，即自然启发的元启发式从灵活性中获得了很大的效用。我讨论了应用优化算法的商业环境中的全球趋势，我推测高度灵活的算法框架可能会在我们多样化和快速变化的世界中变得越来越流行。
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英文标题：
《Survival of the flexible: explaining the recent dominance of
nature-inspired optimization within a rapidly evolving world》
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作者：
James M Whitacre
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最新提交年份：
2011
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Neural and Evolutionary Computing 神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
Although researchers often comment on the rising popularity of nature-inspired meta-heuristics (NIM), there has been a paucity of data to directly support the claim that NIM are growing in prominence compared to other optimization techniques. This study presents evidence that the use of NIM is not only growing, but indeed appears to have surpassed mathematical optimization techniques (MOT) in several important metrics related to academic research activity (publication frequency) and commercial activity (patenting frequency). Motivated by these findings, this article discusses some of the possible origins of this growing popularity. I review different explanations for NIM popularity and discuss why some of these arguments remain unsatisfying. I argue that a compelling and comprehensive explanation should directly account for the manner in which most NIM success has actually been achieved, e.g. through hybridization and customization to different problem environments. By taking a problem lifecycle perspective, this paper offers a fresh look at the hypothesis that nature-inspired meta-heuristics derive much of their utility from being flexible. I discuss global trends within the business environments where optimization algorithms are applied and I speculate that highly flexible algorithm frameworks could become increasingly popular within our diverse and rapidly changing world.
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PDF链接：
https://arxiv.org/pdf/0907.0332
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