- author = "Pedro Ribeiro and Anil Saini and Jay Moran and Nicholas Matsumoto and Hyunjun Choi and Miguel Hernandez and Jason H. Moore",
- title = "{TPOT2}: A New Graph-Based Implementation of the Tree-Based Pipeline Optimization Tool for Automated Machine Learning",
- old_title = "Automated machine learning",
- booktitle = "Genetic Programming Theory and Practice XX",
- year = "2023",
- editor = "Stephan Winkler and Leonardo Trujillo and Charles Ofria and Ting Hu",
- series = "Genetic and Evolutionary Computation",
- pages = "1--17",
- address = "Michigan State University, USA",
- month = jun # " 1-3",
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming, TPOT",
- isbn13 = "978-981-99-8412-1",
-
DOI = "
doi:10.1007/978-981-99-8413-8_1",
-
code_url = "
https://github.com/EpistasisLab/tpot2",
-
code_url = "https://github.com/alegonz/baikal",
-
code_url = "https://github.com/scikit-learn-contrib/skdag",
-
code_url = "https://github.com/epistasisLab/tpot2_gptp_experiments",
- abstract = "we present a new implementation of the popular Tree-Based Pipeline Optimization Tool (TPOT). This new implementation, called TPOT2, was rebuilt from the ground up to be more modular, easier to maintain, and easier to expand. TPOT2 comes with new features and optimisations, such as a more flexible graph-based representation of Scikit-Learn pipelines and the ability to specify various aspects of the evolutionary run. Using experiments on multiple benchmark datasets, we show that TPOT2 performs at least as well as TPOT1 with equivalent settings, with stronger performance on a few datasets. We outline some future directions for further optimizations and applications.",
-
notes = "Pedro Ribeiro 2018 PhD
https://doi.org/10.47749/T/UNICAMP.2018.1062988 not on
GP?
Part of \cite{Hu:2023:GPTP} published after the workshop in 2024",
