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MO-ParamILS: A Multi-objective Automatic Algorithm Configuration Framework

机译:MO-ParamILS:多目标自动算法配置框架

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Automated algorithm configuration procedures play an increasingly important role in the development and application of algorithms for a wide range of computationally challenging problems. Until very recently, these configuration procedures were limited to optimising a single performance objective, such as the running time or solution quality achieved by the algorithm being configured. However, in many applications there is more than one performance objective of interest. This gives rise to the multi-objective automatic algorithm configuration problem, which involves finding a Pareto set of configurations of a given target algorithm that characterises trade-offs between multiple performance objectives. In this work, we introduce MO-ParamILS, a multi-objective extension of the state-of-the-art single-objective algorithm configuration framework ParamILS, and demonstrate that it produces good results on several challenging bi-objective algorithm configuration scenarios compared to a base-line obtained from using a state-of-the-art single-objective algorithm configurator.
机译:自动化的算法配置过程在针对各种计算难题的算法的开发和应用中发挥着越来越重要的作用。直到最近,这些配置过程还仅限于优化单个性能目标,例如通过配置的算法实现的运行时间或解决方案质量。但是,在许多应用程序中,有多个以上的性能目标值得关注。这引起了多目标自动算法配置问题,该问题涉及找到给定目标算法的Pareto配置集合,该集合描述了多个性能目标之间的折衷。在这项工作中,我们介绍了MO-ParamILS,它是最新的单目标算法配置框架ParamILS的多目标扩展,并证明与以下几种方法相比,它在几种具有挑战性的双目标算法配置方案中均能产生良好的结果通过使用最新的单目标算法配置器获得的基线。

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