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arXiv · 2302.00731

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

Nicholas Matsumoto·Anil Kumar Saini·Pedro Ribeiro·Hyunjun Choi·Alena Orlenko·Leo-Pekka Lyytikäinen·Jari O Laurikka·Terho Lehtimäki·Sandra Batista·Jason H. Moore
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Abstract

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.

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Nicholas Matsumoto, Anil Kumar Saini, Pedro Ribeiro, Hyunjun Choi, Alena Orlenko, Leo-Pekka Lyytikäinen, Jari O Laurikka, Terho Lehtimäki, Sandra Batista, Jason H. Moore. 2023-02-01. Faster Convergence with Lexicase Selection in Tree-based Automated Machine Learning. https://arxiv.org/abs/2302.00731

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