- author = "Nicholas Matsumoto and Anil Kumar Saini and Pedro Ribeiro and Hyunjun Choi and Alena Orlenko and Leo-Pekka Lyytikainen and Jari O. Laurikka and Terho Lehtimaki and Sandra Batista and Jason H. Moore",
- title = "Faster Convergence with Lexicase Selection in Tree-based Automated Machine Learning",
- booktitle = "EuroGP 2023: Proceedings of the 26th European Conference on Genetic Programming",
- year = "2023",
- month = "12-14 " # apr,
- editor = "Gisele Pappa and Mario Giacobini and Zdenek Vasicek",
- series = "LNCS",
- volume = "13986",
- publisher = "Springer Verlag",
- address = "Brno, Czech Republic",
- pages = "165--181",
- organisation = "EvoStar, Species",
- keywords = "genetic algorithms, genetic programming, TPOT, Lexicase, Parent Selection, NSGA-II, Convergence, Trie",
- isbn13 = "978-3-031-29572-0",
-
URL = "
https://rdcu.be/c8UUl",
-
DOI = "
doi:10.1007/978-3-031-29573-7_11",
- size = "17 pages",
- 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.",
-
notes = "See also https://arxiv.org/abs/2302.00731
Part of \cite{Pappa:2023:GP} EuroGP'2023 held in conjunction with EvoCOP2023, EvoMusArt2023 and EvoApplications2023",
