Layered TPOT: Speeding up Tree-based Pipeline Optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Misc{DBLP:journals/corr/abs-1801-06007,

• author = "Pieter Gijsbers and Joaquin Vanschoren and Randal S. Olson",
• title = "Layered {TPOT:} Speeding up Tree-based Pipeline Optimization",
• howpublished = "arXiv",
• year = "2018",
• volume = "abs/1801.06007",
• month = "12 " # mar,
• keywords = "genetic algorithms, genetic programming, TPOT",
• URL = "http://arxiv.org/abs/1801.06007",
• timestamp = "Mon, 13 Aug 2018 16:48:02 +0200",
• biburl = "https://dblp.org/rec/journals/corr/abs-1801-06007.bib",
• bibsource = "dblp computer science bibliography, https://dblp.org",
• size = "24 pages",
• abstract = "With the demand for machine learning increasing, so does the demand for tools which make it easier to use. Automated machine learning (AutoML) tools have been developed to address this need, such as the Tree-Based Pipeline Optimization Tool (TPOT) which uses genetic programming to build optimal pipelines. We introduce Layered TPOT, a modification to TPOT which aims to create pipelines equally good as the original, but in significantly less time. This approach evaluates candidate pipelines on increasingly large subsets of the data according to their fitness, using a modified evolutionary algorithm to allow for separate competition between pipelines trained on different sample sizes. Empirical evaluation shows that, on sufficiently large datasets, Layered TPOT indeed finds better models faster.",
• notes = "LTPOT See \cite{DBLP:conf/pkdd/GijsbersVO17}",

}

Genetic Programming entries for Pieter Gijsbers Joaquin Vanschoren Randal S Olson

Citations