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（Dynamic Algorithm Configuration, DAC）探讨了如何以数据驱动方式自动学习策略以控制算法参数的问题。近年来，该问题已获得进化计算领域的广泛关注。因此，构建一个优质的基准测试集合，以从结构上理解不同DAC解决方案的有效性与局限性，具有强烈需求。基于近期提出的具有明确理论性质和真实信息的DAC基准研究工作，本文建议将控制求解OneMax问题的$(1+(\lambda,\lambda))$遗传算法中的关键参数$\lambda$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）自动化算法配置来解决DAC问题，并提出了显著提升该方法性能的技术。我们的方法能够在足够大的问题规模上持续优于先前理论工作推导的默认参数控制策略。此外，我们揭示了参数控制搜索策略的景观新发现，并提出了通过数值逼近真实最优策略来计算更强大基准基线的方法。