Lexicase selection is a successful parent selection method in genetic programming that has outperformed other methods across multiple benchmark suites. Unlike other selection methods that require explicit parameters to function, such as tournament size in tournament selection, lexicase selection does not. However, if evolutionary parameters like population size and number of generations affect the effectiveness of a selection method, then lexicase's performance may also be impacted by these `hidden' parameters. Here, we study how these hidden parameters affect lexicase's ability to exploit gradients and maintain specialists using diagnostic metrics. By varying the population size with a fixed evaluation budget, we show that smaller populations tend to have greater exploitation capabilities, whereas larger populations tend to maintain more specialists. We also consider the effect redundant test cases have on specialist maintenance, and find that high redundancy may hinder the ability to optimize and maintain specialists, even for larger populations. Ultimately, we highlight that population size, evaluation budget, and test cases must be carefully considered for the characteristics of the problem being solved.

翻译：词典选择是遗传编程中一种成功的父代选择方法，在多个基准测试套件中表现优于其他方法。与锦标赛选择等需要显式参数（如锦标赛规模）才能运行的选择方法不同，词典选择无需此类参数。然而，若种群规模、进化代数等演化参数会影响选择方法的有效性，则词典选择的性能也可能受到这些“隐性”参数的影响。本文通过诊断指标研究这些隐性参数如何影响词典选择利用梯度与维持特化个体的能力。在固定评估预算下改变种群规模，我们发现较小种群往往具有更强的开发能力，而较大种群则倾向于维持更多特化个体。我们还考察了冗余测试用例对特化个体维持的影响，发现高冗余度可能阻碍优化与维持特化个体的能力，即使对于较大种群亦是如此。最终我们强调，必须针对待解问题的特性审慎考量种群规模、评估预算与测试用例的配置。