# Faster Convergence with Lexicase Selection in Tree-based Automated Machine Learning

arXiv: https://arxiv.org/abs/2302.00731 · HTML: https://mlsanity.com/paper/2302.00731/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 1 Feb 2023.

## Builds on (most cited first, 3 of 3)

- [Epsilon-Lexicase Selection for Regression](https://mlsanity.com/paper/1905.13266/) (2019)
- [Automating biomedical data science through tree-based pipeline optimization](https://mlsanity.com/paper/1601.07925/) (2016)
- [Generative and reproducible benchmarks for comprehensive evaluation of machine learning classifiers](https://mlsanity.com/paper/2107.06475/) (2021)

## Abstract (arXiv)

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.