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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Notes
- 1.
Individual \(i_1\) dominates \(i_2\) if \(i_1\) is better than or the same as \(i_2\) on all objectives and strictly better than \(i_2\) on at least one objective.
- 2.
https://github.com/EpistasisLab/exploration-trie-tpot including code and supplementary material.
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This work is supported by National Institute of Health grants R01 LM010098 and R01 AG066833.
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Matsumoto, N. et al. (2023). Faster Convergence with Lexicase Selection in Tree-Based Automated Machine Learning. In: Pappa, G., Giacobini, M., Vasicek, Z. (eds) Genetic Programming. EuroGP 2023. Lecture Notes in Computer Science, vol 13986. Springer, Cham. https://doi.org/10.1007/978-3-031-29573-7_11
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