摘要翻译：
本文证明了在只满足少数条件的情况下，进化算法中可以出现简单而重要的协同进化特性。我们发现，基于交互的适应度测量，如适应度（线性）排序允许一种共同进化动力学形式，当1）在排序过程中，解决方案能够交互的变化和2）进化发生在多目标环境中时，可以观察到这种形式。这项研究至少在两个方面有助于模拟进化的研究。首先，它在协同进化和多目标优化之间建立了比以前文献中考虑的更广泛的关系。第二，它证明了共同进化行为的前提条件比以前认为的要弱。特别是，我们的模型表明，共同进化不需要物种之间的直接合作或竞争。此外，我们的实验提供了环境扰动可以驱动共同进化过程的证据；这一结论反映了双相进化理论中提出的论点。在讨论中，我们简要地考虑了我们的结果是如何照亮这个和其他最近的进化论的。
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英文标题：
《Evidence of coevolution in multi-objective evolutionary algorithms》
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作者：
James M Whitacre
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最新提交年份：
2009
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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：
  This paper demonstrates that simple yet important characteristics of coevolution can occur in evolutionary algorithms when only a few conditions are met. We find that interaction-based fitness measurements such as fitness (linear) ranking allow for a form of coevolutionary dynamics that is observed when 1) changes are made in what solutions are able to interact during the ranking process and 2) evolution takes place in a multi-objective environment. This research contributes to the study of simulated evolution in a at least two ways. First, it establishes a broader relationship between coevolution and multi-objective optimization than has been previously considered in the literature. Second, it demonstrates that the preconditions for coevolutionary behavior are weaker than previously thought. In particular, our model indicates that direct cooperation or competition between species is not required for coevolution to take place. Moreover, our experiments provide evidence that environmental perturbations can drive coevolutionary processes; a conclusion that mirrors arguments put forth in dual phase evolution theory. In the discussion, we briefly consider how our results may shed light onto this and other recent theories of evolution.
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PDF链接：
https://arxiv.org/pdf/0907.0329