Configuring irace using surrogate configuration benchmarks
Configuring irace using surrogate configuration benchmarks
复制标题
使用代理配置基准配置 irace
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
T. Stützle
中科院分区：
文献类型：
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作者：
Nguyen Dang;Leslie Pérez Cáceres;P. D. Causmaecker;T. Stützle
近年来，已经开发了几种用于参数化算法的自动配置的工具。这些工具，也称为配置器，有自己的参数，影响他们的搜索行为，使他们的可塑性不同种类的配置任务。这些参数的默认值是根据配置器开发人员的经验手动设置的。研究这些参数的影响或配置它们是非常昂贵的，因为它将需要在配置任务上多次执行这些工具，每次通常需要数小时或数天的计算。在这项工作中，我们使用元调优过程来解决这个问题，基于使用代理基准，评估速度要快得多。本文研究了使用流行的irace配置器作为元配置方法的可行性。我们首先研究的真实的和代理基准之间的一致性，使用三个措施：预测精度的代理模型，同质的基准和重要的算法参数列表。然后，我们使用irace在这些代理上配置irace。实验结果表明，该过程的可行性和一个明显的潜在的改进，其默认配置的irace。
Over the recent years, several tools for the automated configuration of parameterized algorithms have been developed. These tools, also called configurators, have themselves parameters that influence their search behavior and make them malleable to different kinds of configuration tasks. The default values of these parameters are set manually based on the experience of the configurator's developers. Studying the impact of these parameters or configuring them is very expensive as it would require many executions of these tools on configuration tasks, each taking often many hours or days of computation. In this work, we tackle this problem using a meta-tuning process, based on the use of surrogate benchmarks that are much faster to evaluate. This paper studies the feasibility of this process using the popular irace configurator as the method to be meta-configured. We first study the consistency between the real and surrogate benchmarks using three measures: the prediction accuracy of the surrogate models, the homogeneity of the benchmarks and the list of important algorithm parameters. Afterwards, we use irace to configure irace on those surrogates. Experimental results indicate the feasibility of this process and a clear potential improvement of irace over its default configuration.
影响因子：
14.4
作者：
Hutter, Frank;Xu, Lin;Leyton-Brown, Kevin
通讯作者：
Leyton-Brown, Kevin