摘要翻译：
本文研究了选择压力对细胞遗传算法性能的影响。细胞遗传算法是将种群嵌入到环形网格上的遗传算法。这种结构使得到目前为止最好的个体的传播速度减慢，并允许在种群中保留潜在的好的解决方案。为了进一步减缓最佳解的传播，我们提出了两种选择性减压策略。我们在一个困难的优化问题--二次分配问题上实验了这些策略，我们证明了对于这两个问题，控制参数的值都有一个值，它给出了最好的性能。这个最优值不能仅用选择压力来解释，选择压力可以通过时间变化和多样性演化来衡量。这一研究使我们得出结论，除了单一的选择压力测量之外，我们还需要其他工具来解释细胞遗传算法的性能。
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英文标题：
《On the Influence of Selection Operators on Performances in Cellular
  Genetic Algorithms》
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作者：
David Simoncini (I3S), Philippe Collard (I3S), S\'ebastien Verel
  (I3S), Manuel Clergue (I3S)
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最新提交年份：
2008
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分类信息：

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：
  In this paper, we study the influence of the selective pressure on the performance of cellular genetic algorithms. Cellular genetic algorithms are genetic algorithms where the population is embedded on a toroidal grid. This structure makes the propagation of the best so far individual slow down, and allows to keep in the population potentially good solutions. We present two selective pressure reducing strategies in order to slow down even more the best solution propagation. We experiment these strategies on a hard optimization problem, the quadratic assignment problem, and we show that there is a value for of the control parameter for both which gives the best performance. This optimal value does not find explanation on only the selective pressure, measured either by take over time and diversity evolution. This study makes us conclude that we need other tools than the sole selective pressure measures to explain the performances of cellular genetic algorithms.
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PDF链接：
https://arxiv.org/pdf/0804.0852