摘要翻译：
提出了一种遗传算法，该算法中每个种群成员只能根据一定的规则与其邻居改变图式。规则方法和邻域结构采用元胞自动机(CA)策略中的元素。遗传算法种群的每一个成员被分配到一个细胞，交叉只发生在相邻细胞之间，根据预定义的规则。尽管CA和GA方法的结合以前已经出现过，但这里我们依赖于CA固有的自组织特性，而不是并行性。这一概念上的转变将我们引向只包含少数成员的紧凑种群的进化。我们发现，由于该算法能够在这个紧凑的自组织种群中挖掘突变，因此它能够比传统的GA策略更有效地搜索设计空间。因此，避免了早熟收敛，最终结果往往更准确。为了增强其优良的变异能力，还实施了一种重新初始化策略。通过对10个试验函数和两个基准结构工程桁架设计问题的检验，验证了该方法的性能。
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英文标题：
《A Compact Self-organizing Cellular Automata-based Genetic Algorithm》
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作者：
Vasileios Barmpoutis, Gary F. Dargush
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最新提交年份：
2007
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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：
  A Genetic Algorithm (GA) is proposed in which each member of the population can change schemata only with its neighbors according to a rule. The rule methodology and the neighborhood structure employ elements from the Cellular Automata (CA) strategies. Each member of the GA population is assigned to a cell and crossover takes place only between adjacent cells, according to the predefined rule. Although combinations of CA and GA approaches have appeared previously, here we rely on the inherent self-organizing features of CA, rather than on parallelism. This conceptual shift directs us toward the evolution of compact populations containing only a handful of members. We find that the resulting algorithm can search the design space more efficiently than traditional GA strategies due to its ability to exploit mutations within this compact self-organizing population. Consequently, premature convergence is avoided and the final results often are more accurate. In order to reinforce the superior mutation capability, a re-initialization strategy also is implemented. Ten test functions and two benchmark structural engineering truss design problems are examined in order to demonstrate the performance of the method.
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PDF链接：
https://arxiv.org/pdf/0711.2478