Dynamic Algorithm Configuration (DAC) tackles the question of how to automatically learn policies to control parameters of algorithms in a data-driven fashion. This question has received considerable attention from the evolutionary community in recent years. Having a good benchmark collection to gain structural understanding on the effectiveness and limitations of different solution methods for DAC is therefore strongly desirable. Following recent work on proposing DAC benchmarks with well-understood theoretical properties and ground truth information, in this work, we suggest as a new DAC benchmark the controlling of the key parameter $\lambda$ in the $(1+(\lambda,\lambda))$~Genetic Algorithm for solving OneMax problems. We conduct a study on how to solve the DAC problem via the use of (static) automated algorithm configuration on the benchmark, and propose techniques to significantly improve the performance of the approach. Our approach is able to consistently outperform the default parameter control policy of the benchmark derived from previous theoretical work on sufficiently large problem sizes. We also present new findings on the landscape of the parameter-control search policies and propose methods to compute stronger baselines for the benchmark via numerical approximations of the true optimal policies.

翻译：动态算法配置（DAC）旨在解决如何以数据驱动方式自动学习算法参数控制策略的问题。近年来，该问题引起了进化计算领域的广泛关注。因此，构建一个优秀的基准测试集，以深入理解不同DAC求解方法的有效性与局限性，具有重要价值。基于近期提出的具有清晰理论性质与真实信息特性的DAC基准研究工作，本文建议将控制$(1+(\lambda,\lambda))$遗传算法求解OneMax问题时的关键参数$\lambda$作为新的DAC基准。我们研究了如何通过在该基准上使用（静态）自动化算法配置的方法来解决DAC问题，并提出了显著提升该方法性能的技术。在足够大规模的问题实例上，我们的方法能够持续优于前期理论工作推导出的默认参数控制策略。此外，我们还揭示了参数控制策略搜索空间的新特性，并提出了通过数值近似真实最优策略来计算更强基准基线的方法。