使用自动化算法配置进行参数控制
Using Automated Algorithm Configuration for Parameter Control
AI总结：
本文提出以$(1+(\lambda,\lambda))$遗传算法求解OneMax时控制$\lambda$作为新DAC基准，并用静态自动化算法配置及增强技术获得优于默认理论策略的结果。
摘要：
动态算法配置（Dynamic Algorithm Configuration，DAC）解决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，这一问题受到进化计算领域的广泛关注。因此，迫切需要一个良好的基准集合，以从结构上理解不同DAC求解方法的有效性与局限性。继近期提出具有清晰理论性质和真实信息的DAC基准之后，本文提出将求解OneMax问题时对$(1+(\lambda,\lambda))$遗传算法中关键参数$\lambda$的控制作为一个新的DAC基准。我们研究了如何在该基准上通过（静态）自动化算法配置求解DAC问题，并提出了若干可显著提升该方法性能的技术。在足够大的问题规模上，我们的方法能够始终优于由先前理论工作导出的该基准默认参数控制策略。我们还给出了关于参数控制策略搜索景观的新发现，并提出通过对真实最优策略进行数值逼近来为该基准计算更强基线的方法。
英文摘要：
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 $λ$ in the $(1+(λ,λ))$~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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
