摘要翻译：
最大密度静物问题(MDSLP)是一个基于康威生命博弈的硬约束优化问题。它是最近在约束规划界研究的加权约束优化问题的一个主要例子。桶消元(BE)是解决这类约束满足问题常用的完备技术。当应用BE所需的内存过高时，可以使用基于它的启发式方法（命名为mini-buckets）来计算最优解的边界。然而，维数的诅咒使得这些技术对大尺寸问题不实用。针对这种情况，我们提出了一种memetic算法，在MDSLP中，使用BE作为一种机制来重组解，从父集合中提供可能的最佳子集合。在此基础上，进一步研究了精确/元启发式混合与分支定界技术和小桶混合的多层模型。大量的实验结果分析了这些模型和多亲本重组的性能。所得到的算法在比现有方法更短的时间内一致地为最新解决的实例找到最优模式。此外，它表明，该建议提供了新的最佳已知的解决方案，为非常大的实例。
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英文标题：
《Finding Still Lifes with Memetic/Exact Hybrid Algorithms》
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作者：
Jose E. Gallardo, Carlos Cotta, Antonio J. Fernandez
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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：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 maximum density still life problem (MDSLP) is a hard constraint optimization problem based on Conway's game of life. It is a prime example of weighted constrained optimization problem that has been recently tackled in the constraint-programming community. Bucket elimination (BE) is a complete technique commonly used to solve this kind of constraint satisfaction problem. When the memory required to apply BE is too high, a heuristic method based on it (denominated mini-buckets) can be used to calculate bounds for the optimal solution. Nevertheless, the curse of dimensionality makes these techniques unpractical for large size problems. In response to this situation, we present a memetic algorithm for the MDSLP in which BE is used as a mechanism for recombining solutions, providing the best possible child from the parental set. Subsequently, a multi-level model in which this exact/metaheuristic hybrid is further hybridized with branch-and-bound techniques and mini-buckets is studied. Extensive experimental results analyze the performance of these models and multi-parent recombination. The resulting algorithm consistently finds optimal patterns for up to date solved instances in less time than current approaches. Moreover, it is shown that this proposal provides new best known solutions for very large instances.
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PDF链接：
https://arxiv.org/pdf/0812.4170