New Ways to Calibrate Evolutionary Algorithms
New Ways to Calibrate Evolutionary Algorithms
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DOI：
10.1007/978-3-540-72960-0_8
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发表时间：
2008-01-01
期刊：
影响因子：
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通讯作者：
Schut, Martijn C.
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文献类型：
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作者：
Eiben, Gusz;Schut, Martijn C.
进化算法(EA)各种参数的取值问题是保证其良好性能的关键。一种方法是动态控制EA参数，这可以通过各种方式和不同的参数来完成。我们简要回顾了这些选项，并介绍了文献搜索的结果和关于最受欢迎的选项的一些统计数据。此后，我们提供了三个案例研究，表明极有可能出现不常见的变异。特别是，我们建议将重点放在调节选择和种群大小的参数上，而不是关于交叉和突变的参数上。在技术方面，通过实例对调整赛事规模的案例研究表明，全局参数也可以自适应，启发式自适应和纯自适应可以成功地结合成两者的混合。
The issue of setting the values of various parameters of an evolutionary algorithm (EA) is crucial for good performance. One way to do it is by controlling EA parameters on-the-fly, which can be done in various ways and for various parameters. We briefly review these options in general and present the findings of a literature search and some statistics about the most popular options. Thereafter, we provide three case studies indicating a high potential for uncommon variants. In particular, we recommend focusing on parameters regulating selection and population size, rather than those concerning crossover and mutation. On the technical side, the case study on adjusting tournament size shows by example that global parameters can also be self-adapted, and that heuristic adaptation and pure self-adaptation can be successfully combined into a hybrid of the two.