知情降采样词典选择：识别高效训练案例以提升问题求解效率
Informed Down-Sampled Lexicase Selection: Identifying productive training cases for efficient problem solving
AI总结：
本文提出知情降采样词典选择方法，利用种群统计信息构建更具信息量的训练案例子集，在PushGP和语法引导GP上显著优于随机降采样，并能更一致地保留重要案例和特化个体。
摘要：
遗传编程（GP）通常使用大规模训练集，并要求所有个体在选择过程中对所有训练案例进行评估。随机降采样词典选择仅在一个随机子集的训练案例上评估个体，从而在相同程序执行次数下探索更多个体。然而，随机创建降采样可能会在若干代中排除当前降采样中的重要案例，而测量相同行为的案例（同义案例）可能因其冗余性被过度使用。在本工作中，我们提出了知情降采样词典选择（Informed Down-Sampled Lexicase Selection）。该方法利用种群统计信息构建包含更多独特且因此更具信息量的训练案例的降采样。通过在两个不同的GP系统（PushGP和语法引导GP）上进行实证研究，我们发现知情降采样在一组当代程序合成基准问题上显著优于随机降采样。通过对所构建降采样的分析，我们发现重要的训练案例在独立的进化运行和不同系统中被一致地纳入降采样。我们假设这种改进可归因于知情降采样词典选择能够在进化过程中维持更多特化个体，同时受益于降低的单次评估成本。
英文摘要：
Genetic Programming (GP) often uses large training sets and requires all individuals to be evaluated on all training cases during selection. Random down-sampled lexicase selection evaluates individuals on only a random subset of the training cases allowing for more individuals to be explored with the same amount of program executions. However, creating a down-sample randomly might exclude important cases from the current down-sample for a number of generations, while cases that measure the same behavior (synonymous cases) may be overused despite their redundancy. In this work, we introduce Informed Down-Sampled Lexicase Selection. This method leverages population statistics to build down-samples that contain more distinct and therefore informative training cases. Through an empirical investigation across two different GP systems (PushGP and Grammar-Guided GP), we find that informed down-sampling significantly outperforms random down-sampling on a set of contemporary program synthesis benchmark problems. Through an analysis of the created down-samples, we find that important training cases are included in the down-sample consistently across independent evolutionary runs and systems. We hypothesize that this improvement can be attributed to the ability of Informed Down-Sampled Lexicase Selection to maintain more specialist individuals over the course of evolution, while also benefiting from reduced per-evaluation costs.
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