摘要翻译：
为了减少用户疲劳，提出了基于配对比较的交互式差分进化算法(IDE)，并与交互式遗传算法(IGA)和锦标赛IGA进行了比较。用户界面和收敛性能是降低交互式进化计算(IEC)用户疲劳的两大关键。与IGA和传统IDE不同，所提出的IDE和锦标赛IGA的用户不需要对整个个体进行比较，而是对个体进行比较，这在很大程度上减少了用户的疲劳。在本文中，我们设计了一个伪IEC用户，并用IEC模拟器对另一个因素IEC收敛性能进行了评估，结果表明我们提出的IDE比IGA和锦标赛IGA收敛得更快，即我们提出的IDE无论从用户界面还是收敛性能上都优于其他IDE。
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英文标题：
《Paired Comparisons-based Interactive Differential Evolution》
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作者：
Hideyuki Takagi (I3S), Denis Pallez (I3S)
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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：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：
  We propose Interactive Differential Evolution (IDE) based on paired comparisons for reducing user fatigue and evaluate its convergence speed in comparison with Interactive Genetic Algorithms (IGA) and tournament IGA. User interface and convergence performance are two big keys for reducing Interactive Evolutionary Computation (IEC) user fatigue. Unlike IGA and conventional IDE, users of the proposed IDE and tournament IGA do not need to compare whole individuals each other but compare pairs of individuals, which largely decreases user fatigue. In this paper, we design a pseudo-IEC user and evaluate another factor, IEC convergence performance, using IEC simulators and show that our proposed IDE converges significantly faster than IGA and tournament IGA, i.e. our proposed one is superior to others from both user interface and convergence performance points of view.
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PDF链接：
https://arxiv.org/pdf/0909.2091