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摘要翻译：
生物系统的相互作用网络具有许多非随机的结构特性，其中一些特性被认为对系统的鲁棒性有积极的影响。研究人员刚刚开始理解这些结构特性是如何出现的，然而组件适应性和社区发展（模块化）的建议角色已经引起了科学界的兴趣。在本研究中，我们将其中的一些概念应用到进化算法中，并利用种群在适应度景观上移动时接收到的信息自发地组织其种群。更准确地说，我们使用适应度和基于聚类的驱动力来指导网络结构动力学，而网络结构动力学又由进化算法的种群动力学控制。为了评估这对进化的影响，对六个工程设计问题和六个人工测试函数进行了实验，并与细胞遗传算法和其他16种进化算法设计进行了比较。我们的结果表明，自组织拓扑进化算法表现出惊人的鲁棒搜索行为，在短时间和长时间尺度上都有很好的性能。通过对这些结果的仔细分析，我们得出结论，种群与其拓扑之间的协同进化为设计鲁棒搜索启发式提供了一个强有力的新范式。
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英文标题：
《Spontaneous organization leads to robustness in evolutionary algorithms》
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作者：
James M. Whitacre, Ruhul A. Sarker, Q. Tuan Pham
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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：
The interaction networks of biological systems are known to take on several non-random structural properties, some of which are believed to positively influence system robustness. Researchers are only starting to understand how these structural properties emerge, however suggested roles for component fitness and community development (modularity) have attracted interest from the scientific community. In this study, we apply some of these concepts to an evolutionary algorithm and spontaneously organize its population using information that the population receives as it moves over a fitness landscape. More precisely, we employ fitness and clustering based driving forces for guiding network structural dynamics, which in turn are controlled by the population dynamics of an evolutionary algorithm. To evaluate the effect this has on evolution, experiments are conducted on six engineering design problems and six artificial test functions and compared against cellular genetic algorithms and 16 other evolutionary algorithm designs. Our results indicate that a self-organizing topology evolutionary algorithm exhibits surprisingly robust search behavior with promising performance observed over short and long time scales. After a careful analysis of these results, we conclude that the coevolution between a population and its topology represents a powerful new paradigm for designing robust search heuristics.
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PDF链接：
https://arxiv.org/pdf/0907.0507
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