摘要翻译：
本文介绍了几种用于实域全局优化的进化算法。人们的兴趣集中在多模态问题上，这些问题通常会出现早熟收敛的困难。首先，简要回顾了标准遗传算法(SGA)的实数二进制编码及其在多峰问题中的不理想行为，并对其在克服早熟收敛方面的一些改进进行了综述。详细研究了两种基于微分算子的实数编码方法：由R.Storn和K.Price首次提出的非常现代有效的方法--微分进化(DE)和作者提出的简化实数编码微分遗传算法SADE。此外，还研究了SADE方法的一种改进，称为CERAF技术，使解的种群能够逃离局部极值。所有方法都在相同的目标函数集上进行了测试，并提出了一种基于可靠方法的系统比较。事实证明，实数编码方法在实域上表现出比二进制算法更好的性能，即使经过几次改进也是如此。此外，由于微分算子的自适应可能性，证明了它们的积极影响。从可靠性的角度看，用本文所述技术改进的实编码差分算法似乎是一种通用的、可靠的方法，能够解决所有提出的测试问题。
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英文标题：
《Improvements of real coded genetic algorithms based on differential
  operators preventing premature convergence》
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作者：
O. Hrstka and A. Kucerova
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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：
  This paper presents several types of evolutionary algorithms (EAs) used for global optimization on real domains. The interest has been focused on multimodal problems, where the difficulties of a premature convergence usually occurs. First the standard genetic algorithm (SGA) using binary encoding of real values and its unsatisfactory behavior with multimodal problems is briefly reviewed together with some improvements of fighting premature convergence. Two types of real encoded methods based on differential operators are examined in detail: the differential evolution (DE), a very modern and effective method firstly published by R. Storn and K. Price, and the simplified real-coded differential genetic algorithm SADE proposed by the authors. In addition, an improvement of the SADE method, called CERAF technology, enabling the population of solutions to escape from local extremes, is examined. All methods are tested on an identical set of objective functions and a systematic comparison based on a reliable methodology is presented. It is confirmed that real coded methods generally exhibit better behavior on real domains than the binary algorithms, even when extended by several improvements. Furthermore, the positive influence of the differential operators due to their possibility of self-adaptation is demonstrated. From the reliability point of view, it seems that the real encoded differential algorithm, improved by the technology described in this paper, is a universal and reliable method capable of solving all proposed test problems.
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PDF链接：
https://arxiv.org/pdf/0902.1629