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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:自动算法配置程序在算法的开发和应用方面发挥着越来越重要的作用,以实现广泛的计算具有挑战性的问题。直到最近,这些配置过程仅限于优化单个性能目标,例如通过配置算法实现的运行时间或解决方案质量。但是,在许多应用中,有一个以上的绩效目标。这导致了多目标自动算法配置问题,这涉及查找给定目标算法的Pareto一组配置,其特征在多个性能目标之间的权衡。在这项工作中,我们介绍了MO-PARAMILS,是最先进的单目标算法配置框架发行者的多目标扩展,并证明它在几个具有挑战性的双目标算法配置方案上产生了良好的结果。相比从使用最先进的单目标算法配置器获得的碱基。

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