摘要翻译：
本文提出了一种求解集合覆盖问题的新型遗传算法。它不同于以往的进化方法，首先因为它是一种间接算法，即通过外部解码器函数找到实际解。遗传算法本身为解码器提供了解变量和其他参数的置换。其次，通过添加另一个间接优化层，可以进一步改进结果。解码器不会直接寻找低成本的解决方案，而是以好的可开发的解决方案为目标。然后通过另一个爬山算法对这些算法进行优化。虽然看起来更复杂，但我们将表明这种三阶段方法在解决质量、速度和对新类型问题的适应性方面比更直接的方法更有优势。给出了大量的计算结果，并与最新的进化和其他启发式方法对相同的数据实例进行了比较。
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英文标题：
《An Indirect Genetic Algorithm for Set Covering Problems》
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作者：
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：
  This paper presents a new type of genetic algorithm for the set covering problem. It differs from previous evolutionary approaches first because it is an indirect algorithm, i.e. the actual solutions are found by an external decoder function. The genetic algorithm itself provides this decoder with permutations of the solution variables and other parameters. Second, it will be shown that results can be further improved by adding another indirect optimisation layer. The decoder will not directly seek out low cost solutions but instead aims for good exploitable solutions. These are then post optimised by another hill-climbing algorithm. Although seemingly more complicated, we will show that this three-stage approach has advantages in terms of solution quality, speed and adaptability to new types of problems over more direct approaches. Extensive computational results are presented and compared to the latest evolutionary and other heuristic approaches to the same data instances.
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PDF链接：
https://arxiv.org/pdf/0803.2965