Transfer learning: a building block selection mechanism in genetic programming for symbolic regression
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{Muller:2019:GECCOcomp,
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author = "Brandon Muller and Harith Al-Sahaf and Bing Xue and
Mengjie Zhang",
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title = "Transfer learning: a building block selection
mechanism in genetic programming for symbolic
regression",
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booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary
Computation Conference Companion",
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year = "2019",
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editor = "Richard Allmendinger and Carlos Cotta and
Carola Doerr and Pietro S. Oliveto and Thomas Weise and
Ales Zamuda and Anne Auger and Dimo Brockhoff and
Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and
David Camacho-Fernandez and Massimiliano Vasile and
Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and
Saul Zapotecas and Qingfu Zhang and Ozgur Akman and
Khulood Alyahya and Juergen Branke and
Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and
Josu {Ceberio Uribe} and Valentino Santucci and
Marco Baioletti and John McCall and Emma Hart and
Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and
Chika Oshima and Stefan Wagner and
Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and
Pascal Kerschke and Boris Naujoks and Vanessa Volz and
Anna I Esparcia-Alcazar and Riyad Alshammari and
Erik Hemberg and Tokunbo Makanju and Brad Alexander and
Saemundur O. Haraldsson and Markus Wagner and
Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and
Thomas Stuetzle and David Walker and Matt Johns and
Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and
Takato Tatsumi and Nadarajen Veerapen and
Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and
Stephen Smith and Stefano Cagnoni and
Robert M. Patton and William {La Cava} and Randal Olson and
Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and
Koji Shimoyama and Hemant Kumar Singh and
Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and
Richard Everson and Handing Wang and Yaochu Jin and
Marcus Gallagher and Mike Preuss and
Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale",
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isbn13 = "978-1-4503-6748-6",
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pages = "350--351",
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address = "Prague, Czech Republic",
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DOI = "doi:10.1145/3319619.3322072",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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month = "13-17 " # jul,
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organisation = "SIGEVO",
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keywords = "genetic algorithms, genetic programming",
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abstract = "In machine learning, transfer learning is concerned
with using prior knowledge as a way to improve the
process of training a new model in a different, but
related, domain. Transfer learning has been shown to be
beneficial across a large set of problems. One of the
main questions any transfer learning approach must
address is What to transfer?. This paper proposes a new
transfer learning method in genetic programming (GP) to
improve solving symbolic regression problems by
extracting all potentially good and unique building
blocks from a source problem. The proposed method is
compared against standard GP and a state-of-the-art GP
method on ten regression datasets. The experimental
results show that the proposed method has achieved
significantly better or comparable performance to that
of the competitive methods. Furthermore, the proposed
method shows better initial population and convergence
compared to the other methods.",
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notes = "Also known as \cite{3322072} GECCO-2019 A
Recombination of the 28th International Conference on
Genetic Algorithms (ICGA) and the 24th Annual Genetic
Programming Conference (GP)",
- }
Genetic Programming entries for
Brandon Muller
Harith Al-Sahaf
Bing Xue
Mengjie Zhang
Citations