In many evolutionary computation systems, parent selection methods can affect, among other things, convergence to a solution. In this paper, we present a study comparing the role of two commonly used parent selection methods in evolving machine learning pipelines in an automated machine learning system called Tree-based Pipeline Optimization Tool (TPOT). Specifically, we demonstrate, using experiments on multiple datasets, that lexicase selection leads to significantly faster convergence as compared to NSGA-II in TPOT. We also compare the exploration of parts of the search space by these selection methods using a trie data structure that contains information about the pipelines explored in a particular run.

翻译：在许多进化计算系统中，父代选择方法会影响收敛到解的速度等多种特性。本文通过实验对比了两种常用父代选择方法在自动机器学习系统——树结构流水线优化工具（TPOT）中进化机器学习流水线时的作用。具体而言，我们基于多个数据集的实验证明，与TPOT中的NSGA-II相比，词典选择能够显著加速收敛。我们还利用一种包含特定运行中探索过的流水线信息的字典树数据结构，比较了这些选择方法对搜索空间各区域的探索程度。