Transfer learning: a building block selection mechanism in genetic programming for symbolic regression

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Muller:2019:GECCOcomp,

• author = "Brandon Muller and Harith Al-Sahaf and Bing Xue and Mengjie Zhang",
• title = "Transfer learning: a building block selection mechanism in genetic programming for symbolic regression",
• booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion",
• year = "2019",
• 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",
• isbn13 = "978-1-4503-6748-6",
• pages = "350--351",
• address = "Prague, Czech Republic",
• DOI = "doi:10.1145/3319619.3322072",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• month = "13-17 " # jul,
• organisation = "SIGEVO",
• keywords = "genetic algorithms, genetic programming",
• 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.",
• 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