We present an analysis of the loss of population-level test coverage induced by different down-sampling strategies when combined with lexicase selection. We study recorded populations from the first generation of genetic programming runs, as well as entirely synthetic populations. Our findings verify the hypothesis that informed down-sampling better maintains population-level test coverage when compared to random down-sampling. Additionally, we show that both forms of down-sampling cause greater test coverage loss than standard lexicase selection with no down-sampling. However, given more information about the population, we found that informed down-sampling can further reduce its test coverage loss. We also recommend wider adoption of the static population analyses we present in this work.
翻译:翻译摘要:本文针对结合字典选择法的不同下采样策略,在保持种群级测试覆盖率的损失方面进行了分析。我们研究了来自遗传编程第一代运行的记录种群以及完全合成的种群。我们的研究结果证实了一种假设,即与随机采样相比,知情采样更好地保持种群级测试覆盖率。此外,我们发现两种形式的下采样都比没有下采样的标准字典选择法更容易造成测试覆盖率的损失。然而,在获得更多种群信息的情况下,我们发现知情采样可以进一步降低其测试覆盖率的损失。我们还推荐广泛采用本文所提供的静态种群分析方法。