摘要翻译：
本文以带软时间窗的车辆路径问题为例，研究了遗传算法在多目标组合优化(MOCO)中的有效性。问题结构是否以及如何影响遗传算法的不同结构的有效性是本研究的动机。给出了不同类型车辆路径问题的计算结果，这些问题的复盖范围随时间窗、时间窗大小、分布和顾客数量的变化而变化。将结果与一种简单有效的多目标组合优化问题局部搜索方法进行了比较。
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英文标题：
《A Computational Study of Genetic Crossover Operators for Multi-Objective
  Vehicle Routing Problem with Soft Time Windows》
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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 article describes an investigation of the effectiveness of genetic algorithms for multi-objective combinatorial optimization (MOCO) by presenting an application for the vehicle routing problem with soft time windows. The work is motivated by the question, if and how the problem structure influences the effectiveness of different configurations of the genetic algorithm. Computational results are presented for different classes of vehicle routing problems, varying in their coverage with time windows, time window size, distribution and number of customers. The results are compared with a simple, but effective local search approach for multi-objective combinatorial optimization problems.
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PDF链接：
https://arxiv.org/pdf/0809.0410