显式学习：人类调度算法的发展方向

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摘要翻译：
调度问题一般是NP-hard组合问题，人们对启发式求解这些问题进行了大量研究。然而，以往的调度方法大多是针对具体问题的，对通用调度算法的研究还处于起步阶段。遗传算法模拟了优胜劣汰的自然进化过程，近年来在解决困难的调度问题方面引起了广泛的关注。在使用GAS时存在一些障碍：没有规范的机制来处理约束，这在大多数现实世界的调度问题中都是常见的，并且很难对一个解决方案进行小的修改。为了克服这两个困难，在[1]和[2]中提出了护士调度和司机调度的间接方法，其中GAs通过映射解空间来使用，并通过单独的解码例程来构建原始问题的解。
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英文标题：
《Explicit Learning: an Effort towards Human Scheduling Algorithms》
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作者：
Jingpeng Li and Uwe Aickelin
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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：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：
  Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy.   Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) have attracted much attention in solving difficult scheduling problems in recent years. Some obstacles exist when using GAs: there is no canonical mechanism to deal with constraints, which are commonly met in most real-world scheduling problems, and small changes to a solution are difficult. To overcome both difficulties, indirect approaches have been presented (in [1] and [2]) for nurse scheduling and driver scheduling, where GAs are used by mapping the solution space, and separate decoding routines then build solutions to the original problem.
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PDF链接：
https://arxiv.org/pdf/0804.0580

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关键词：发展方向 Evolutionary Presentation Intelligence Difficulties 处于 been Explicit 障碍 构建