In genetic programming, parent selection methods are employed to select promising candidate individuals from the current generation that can be used as parents for the next generation. These algorithms can affect, sometimes indirectly, whether or not individuals containing certain programming constructs, such as loops, are selected and propagated in the population. This in turn can affect the chances that the population will produce a solution to the problem. In this paper, we present the results of the experiments using three different parent selection methods on four benchmark program synthesis problems. We analyze the relationships between the selection methods, the numbers of individuals in the population that make use of loops, and success rates. The results show that the support for the selection of specialists is associated both with the use of loops in evolving populations and with higher success rates.

在遗传编程中，使用亲本选择方法从当前一代中选择有前途的候选个体，可以用作下一代的亲本。这些算法有时会间接影响包含某些编程结构（例如循环）的个体是否在群体中被选择和传播。这反过来又会影响人口解决问题的机会。在本文中，我们展示了在四个基准程序综合问题上使用三种不同的父选择方法的实验结果。我们分析了选择方法、群体中使用循环的个体数量和成功率之间的关系。