摘要翻译：
本文介绍了遗传算法求解多目标优化问题的一般方法。利用群体个体间的特殊优势关系定义适应度算子，使遗传算法能够解决具有高效但凸优势方案的问题。该算法在多语言计算机程序中实现，解决了多目标下带时间窗的车辆路径问题。该程序的图形用户界面显示了遗传算法的进展情况，并且可以方便地修改算法的主要参数。此外，该程序还为决策者提供了强有力的决策支持。该软件已经证明了它在欧洲学术软件奖EASA决赛中的卓越，在基布尔学院/牛津大学/英国举行。
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英文标题：
《Genetic Algorithms for multiple objective vehicle routing》
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作者：
Martin Josef Geiger
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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：
  The talk describes a general approach of a genetic algorithm for multiple objective optimization problems. A particular dominance relation between the individuals of the population is used to define a fitness operator, enabling the genetic algorithm to adress even problems with efficient, but convex-dominated alternatives. The algorithm is implemented in a multilingual computer program, solving vehicle routing problems with time windows under multiple objectives. The graphical user interface of the program shows the progress of the genetic algorithm and the main parameters of the approach can be easily modified. In addition to that, the program provides powerful decision support to the decision maker. The software has proved it's excellence at the finals of the European Academic Software Award EASA, held at the Keble college/ University of Oxford/ Great Britain.
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PDF链接：
https://arxiv.org/pdf/0809.0416