摘要翻译：
在交互式进化计算的评估过程中，我们描述了一种新的算法，该算法通过结合眼睛跟踪器来最大限度地减少用户的疲劳。然后将该方法应用于交互式一极大优化问题。
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英文标题：
《Eye-Tracking Evolutionary Algorithm to minimize user's fatigue in IEC
  applied to Interactive One-Max problem》
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作者：
Denis Pallez (LIRIS), Philippe Collard (I3S), Thierry Baccino (LPEQ),
  Laurent Dumercy (LPEQ)
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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：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：
  In this paper, we describe a new algorithm that consists in combining an eye-tracker for minimizing the fatigue of a user during the evaluation process of Interactive Evolutionary Computation. The approach is then applied to the Interactive One-Max optimization problem.
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PDF链接：
https://arxiv.org/pdf/0803.3192