自然激发的相互作用网络的自组织 优化

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摘要翻译：
在过去的十年里，在理解复杂的生物系统方面取得了显著的进展，但是很少有人试图将这些知识纳入自然启发的优化算法。在本文中，我们首次尝试将复杂生物系统的一些基本结构特性结合起来，这些特性被认为是系统质量如鲁棒性的必要前提条件。特别地，我们重点研究了进化算法种群中缺少的两个重要条件；局部性和交互上位性的自组织定义。我们证明，当这两个特征结合起来时，提供了规范进化算法或具有结构种群的进化算法（如细胞遗传算法）中没有观察到的算法行为。算法行为中最明显的变化是一种前所未有的能力，可以让基因不同的个体在单个种群中持续共存。这种持续遗传多样性的能力不是强加给种群的，而是作为系统动态的自然结果出现的。
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英文标题：
《The Self-Organization of Interaction Networks for Nature-Inspired
  Optimization》
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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：
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：
  Over the last decade, significant progress has been made in understanding complex biological systems, however there have been few attempts at incorporating this knowledge into nature inspired optimization algorithms. In this paper, we present a first attempt at incorporating some of the basic structural properties of complex biological systems which are believed to be necessary preconditions for system qualities such as robustness. In particular, we focus on two important conditions missing in Evolutionary Algorithm populations; a self-organized definition of locality and interaction epistasis. We demonstrate that these two features, when combined, provide algorithm behaviors not observed in the canonical Evolutionary Algorithm or in Evolutionary Algorithms with structured populations such as the Cellular Genetic Algorithm. The most noticeable change in algorithm behavior is an unprecedented capacity for sustainable coexistence of genetically distinct individuals within a single population. This capacity for sustained genetic diversity is not imposed on the population but instead emerges as a natural consequence of the dynamics of the system.
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PDF链接：
https://arxiv.org/pdf/0907.0334

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关键词：相互作用 自组织 Evolutionary Optimization Intelligence populations 起来 优化 结构 such