摘要翻译：
进化计算和机器学习领域的进展速度目前受到限制--在前一个领域，当进化算法的适应能力被很少理解时，不可能对其进行有利的扩展；在后一个领域，难以找到将困难的现实世界问题有效地半原则性地简化为相对简单的优化问题。在本文中，我们解释了为什么一个理论可以准确地解释简单遗传算法的显著适应能力，有潜力解决这两个限制。我们描述了我们认为的阻碍--历史的和分析的--发现这样一个理论，并强调了积木假说(BBH)所起的消极作用。根据实验结果，我们认为，一个被广泛认为限制SGA自适应能力的基本限制（BBH强烈暗示了这一点）实际上是幻觉的，并不存在。因此，SGA比目前认为的更强大。我们给出了当基因组较长时，用数值方法逼近和研究无限种群SGA在多代上的搜索分布的多元边际是可行的条件，并解释了为什么这一分析与SGA的显着自适应能力之谜有关。
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英文标题：
《Towards a Sound Theory of Adaptation for the Simple Genetic Algorithm》
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作者：
Keki Burjorjee
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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：
  The pace of progress in the fields of Evolutionary Computation and Machine Learning is currently limited -- in the former field, by the improbability of making advantageous extensions to evolutionary algorithms when their capacity for adaptation is poorly understood, and in the latter by the difficulty of finding effective semi-principled reductions of hard real-world problems to relatively simple optimization problems. In this paper we explain why a theory which can accurately explain the simple genetic algorithm's remarkable capacity for adaptation has the potential to address both these limitations. We describe what we believe to be the impediments -- historic and analytic -- to the discovery of such a theory and highlight the negative role that the building block hypothesis (BBH) has played. We argue based on experimental results that a fundamental limitation which is widely believed to constrain the SGA's adaptive ability (and is strongly implied by the BBH) is in fact illusionary and does not exist. The SGA therefore turns out to be more powerful than it is currently thought to be. We give conditions under which it becomes feasible to numerically approximate and study the multivariate marginals of the search distribution of an infinite population SGA over multiple generations even when its genomes are long, and explain why this analysis is relevant to the riddle of the SGA's remarkable adaptive abilities.
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PDF链接：
https://arxiv.org/pdf/0711.1401