变异率对进化算法计算时间的影响 动态优化

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摘要翻译：
传统上，变异被认为是进化算法中的一个重要算子。特别是，已经有许多实验研究表明，自适应变异率对各种静态优化问题是有效的。鉴于自适应和自适应变异对静态优化问题的有效性，有人认为自适应和自适应变异对动态优化问题更有好处，因为自适应和自适应能够遵循动态环境。然而，在对动态优化问题的进化算法进行严格分析方面，很少有理论成果。目前还不清楚自适应和自适应变异率何时可能用于求解动态优化问题的进化算法。本文首次严格分析了自适应变异及其对进化算法求解某些动态优化问题计算量的影响。更具体地说，对于基于个体和基于群体的EAs，我们已经证明了在一些动态优化问题的实例中，任何时间可变的变异率方案都不会显著优于固定的变异率方案。这些证明还提供了对任何时变变异方案不太可能有用的条件的一些见解，以及对问题特征和算法特征之间的关系（例如，不同的变异方案）的一些见解。
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英文标题：
《The Impact of Mutation Rate on the Computation Time of Evolutionary
  Dynamic Optimization》
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作者：
Tianshi Chen, Yunji Chen, Ke Tang, Guoliang Chen, and Xin Yao
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最新提交年份：
2011
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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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一级分类：Computer Science        计算机科学
二级分类：Computational Complexity        计算复杂度
分类描述：Covers models of computation, complexity classes, structural complexity, complexity tradeoffs, upper and lower bounds. Roughly includes material in ACM Subject Classes F.1 (computation by abstract devices), F.2.3 (tradeoffs among complexity measures), and F.4.3 (formal languages), although some material in formal languages may be more appropriate for Logic in Computer Science. Some material in F.2.1 and F.2.2, may also be appropriate here, but is more likely to have Data Structures and Algorithms as the primary subject area.
涵盖计算模型，复杂度类别，结构复杂度，复杂度折衷，上限和下限。大致包括ACM学科类F.1（抽象设备的计算）、F.2.3（复杂性度量之间的权衡）和F.4.3（形式语言）中的材料，尽管形式语言中的一些材料可能更适合于计算机科学中的逻辑。在F.2.1和F.2.2中的一些材料可能也适用于这里，但更有可能以数据结构和算法作为主要主题领域。
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英文摘要：
  Mutation has traditionally been regarded as an important operator in evolutionary algorithms. In particular, there have been many experimental studies which showed the effectiveness of adapting mutation rates for various static optimization problems. Given the perceived effectiveness of adaptive and self-adaptive mutation for static optimization problems, there have been speculations that adaptive and self-adaptive mutation can benefit dynamic optimization problems even more since adaptation and self-adaptation are capable of following a dynamic environment. However, few theoretical results are available in analyzing rigorously evolutionary algorithms for dynamic optimization problems. It is unclear when adaptive and self-adaptive mutation rates are likely to be useful for evolutionary algorithms in solving dynamic optimization problems. This paper provides the first rigorous analysis of adaptive mutation and its impact on the computation times of evolutionary algorithms in solving certain dynamic optimization problems. More specifically, for both individual-based and population-based EAs, we have shown that any time-variable mutation rate scheme will not significantly outperform a fixed mutation rate on some dynamic optimization problem instances. The proofs also offer some insights into conditions under which any time-variable mutation scheme is unlikely to be useful and into the relationships between the problem characteristics and algorithmic features (e.g., different mutation schemes).
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PDF链接：
https://arxiv.org/pdf/1106.0566

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关键词：动态优化 进化算法 Optimization Evolutionary relationship 理论 影响 方案 变异 there