Genetic Programming as an Innovation Engine for Automated Machine Learning: The Tree-Based Pipeline Optimization Tool (TPOT)
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InCollection{Moore:2023:hbeml,
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author = "Jason H. Moore and Pedro H. Ribeiro and
Nicholas Matsumoto and Anil K. Saini",
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title = "Genetic Programming as an Innovation Engine for
Automated Machine Learning: The Tree-Based Pipeline
Optimization Tool (TPOT)",
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booktitle = "Handbook of Evolutionary Machine Learning",
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publisher = "Springer Nature",
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year = "2023",
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editor = "Wolfgang Banzhaf and Penousal Machado and
Mengjie Zhang",
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series = "Genetic and Evolutionary Computation (GEVO)",
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pages = "439--455",
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address = "Singapore",
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edition = "1",
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month = "2 " # nov,
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keywords = "genetic algorithms, genetic programming, TPOT",
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isbn13 = "978-981-99-3813-1",
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ISSN = "1932-0167",
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URL = "https://link.springer.com/book/10.1007/978-981-99-3814-8",
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DOI = "doi:10.1007/978-981-99-3814-8_14",
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abstract = "One of the central challenges of machine learning is
the selection of methods for feature selection
selection, feature engineering, and classification or
regression algorithms for building an analytics
pipeline. This is true for both novices and experts.
Automated machine learning (AutoML) has emerged as a
useful approach to generate machine learning pipelines
without the need for manual construction and
evaluation. We review here some challenges of building
pipelines and present several of the first and most
widely used AutoML methods and open-source software. We
present in detail the Tree-based Pipeline Optimization
Tool (TPOT) that represents pipelines as expression
trees and uses genetic programming (GP) for discovery
and optimisation. We present some of the extensions of
TPOT and its application to real-world big data. We end
with some thoughts about the future of AutoML and
evolutionary machine learning.",
- }
Genetic Programming entries for
Jason H Moore
Pedro Henrique Ribeiro
Nicholas Matsumoto
Anil Kumar Saini
Citations