Program Search for Machine Learning Pipelines Leveraging Symbolic Planning and Reinforcement Learning
Created by W.Langdon from
gp-bibliography.bib Revision:1.7989
- @InProceedings{yang:2018:GPTP,
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author = "Fangkai Yang and Steven Gustafson and
Alexander Elkholy and Daoming Lyu and Bo Liu",
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title = "Program Search for Machine Learning Pipelines
Leveraging Symbolic Planning and Reinforcement
Learning",
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booktitle = "Genetic Programming Theory and Practice XVI",
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year = "2018",
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editor = "Wolfgang Banzhaf and Lee Spector and Leigh Sheneman",
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pages = "209--231",
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address = "Ann Arbor, USA",
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month = "17-20 " # may,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-030-04734-4",
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URL = "http://link.springer.com/chapter/10.1007/978-3-030-04735-1_11",
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DOI = "doi:10.1007/978-3-030-04735-1_11",
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abstract = "In this paper we investigate an alternative knowledge
representation and learning strategy for the automated
machine learning (AutoML) task. Our approach combines a
symbolic planner with reinforcement learning to evolve
programs that process data and train machine learning
classifiers. The planner, which generates all feasible
plans from the initial state to the goal state, gives
preference first to shortest programs and then later to
ones that maximize rewards. The results demonstrate the
efficacy of the approach for finding good machine
learning pipelines, while at the same time showing that
the representation can be used to infer new knowledge
relevant for the problem instances being solved. These
insights can be useful for other automatic programming
approaches, like genetic programming (GP) and Bayesian
optimization pipeline learning, with respect to
representation and learning strategies.",
- }
Genetic Programming entries for
Fangkai Yang
Steven M Gustafson
Alexander Elkholy
Daoming Lyu
Bo Liu
Citations