Genetic programming systems often use large training sets to evaluate the quality of candidate solutions for selection. However, evaluating populations on large training sets can be computationally expensive. Down-sampling training sets has long been used to decrease the computational cost of evaluation in a wide range of application domains. Indeed, recent studies have shown that both random and informed down-sampling can substantially improve problem-solving success for GP systems that use the lexicase parent selection algorithm. We use the PushGP framework to experimentally test whether these down-sampling techniques can also improve problem-solving success in the context of two other commonly used selection methods, fitness-proportionate and tournament selection, across eight GP problems (four program synthesis and four symbolic regression). We verified that down-sampling can benefit the problem-solving success of both fitness-proportionate and tournament selection. However, the number of problems wherein down-sampling improved problem-solving success varied by selection scheme, suggesting that the impact of down-sampling depends both on the problem and choice of selection scheme. Surprisingly, we found that down-sampling was most consistently beneficial when combined with lexicase selection as compared to tournament and fitness-proportionate selection. Overall, our results suggest that down-sampling should be considered more often when solving test-based GP problems.

翻译：遗传编程系统通常使用大型训练集来评估候选解的质量以进行选择。然而，在大规模训练集上评估种群可能具有较高的计算成本。长期以来，下采样训练集被广泛应用于各种应用领域，以降低评估的计算成本。事实上，近期研究表明，对于采用词法选择父代算法的遗传编程系统，随机下采样和知情下采样均能显著提升问题求解成功率。我们利用PushGP框架，在八个遗传编程问题（四个程序合成与四个符号回归）中，通过实验检验这些下采样技术是否也能在另外两种常用选择方法（适应度比例选择与锦标赛选择）的背景下提升问题求解成功率。我们证实下采样有利于适应度比例选择和锦标赛选择的问题求解成功率。然而，下采样能够提升问题求解成功率的问题数量因选择方案而异，这表明下采样的影响既取决于问题本身也取决于选择方案的选取。令人惊讶的是，我们发现与锦标赛选择和适应度比例选择相比，下采样与词法选择相结合时具有最稳定的一致性优势。总体而言，我们的结果表明，在解决基于测试的遗传编程问题时，应更频繁地考虑使用下采样技术。