Evolving Genetic Programming Trees in a Rule-Based Learning Framework
Created by W.Langdon from
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- @InProceedings{Verma:2020:GECCOcomp,
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author = "Siddharth Verma and Piyush Borole and
Ryan Urbanowicz",
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title = "Evolving Genetic Programming Trees in a Rule-Based
Learning Framework",
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year = "2020",
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editor = "Richard Allmendinger and Hugo Terashima Marin and
Efren Mezura Montes and Thomas Bartz-Beielstein and
Bogdan Filipic and Ke Tang and David Howard and
Emma Hart and Gusz Eiben and Tome Eftimov and
William {La Cava} and Boris Naujoks and Pietro Oliveto and
Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and
Xiaodong Li and Saul Zapotecas and Qingfu Zhang and
Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and
Hisao Ishibuchi and Jonathan Fieldsend and
Ozgur Akman and Khulood Alyahya and Juergen Branke and
John R. Woodward and Daniel R. Tauritz and Marco Baioletti and
Josu Ceberio Uribe and John McCall and
Alfredo Milani and Stefan Wagner and Michael Affenzeller and
Bradley Alexander and Alexander (Sandy) Brownlee and
Saemundur O. Haraldsson and Markus Wagner and
Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and
Pablo {Valledor Pellicer} and Thomas Stuetzle and
Matthew Johns and Nick Ross and Ed Keedwell and
Herman Mahmoud and David Walker and Anthony Stein and
Masaya Nakata and David Paetzel and Neil Vaughan and
Stephen Smith and Stefano Cagnoni and Robert M. Patton and
Ivanoe {De Falco} and Antonio {Della Cioppa} and
Umberto Scafuri and Ernesto Tarantino 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
Erik Hemberg and Riyad Alshammari and
Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and
Ponnuthurai Nagaratnam and Roman Senkerik",
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isbn13 = "9781450371278",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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URL = "https://doi.org/10.1145/3377929.3390071",
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DOI = "doi:10.1145/3377929.3390071",
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booktitle = "Proceedings of the 2020 Genetic and Evolutionary
Computation Conference Companion",
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pages = "233--234",
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size = "2 pages",
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keywords = "genetic algorithms, genetic programming, co-evolution,
symbolic regression, learning classifier systems,
rule-based machine learning",
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address = "internet",
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series = "GECCO '20",
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month = jul # " 8-12",
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organisation = "SIGEVO",
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abstract = "Rule-based machine learning (RBML) algorithms such as
learning classifier systems (LCS) are well suited to
classification problems with complex interactions and
heterogeneous associations. Alternatively, genetic
programming (GP) has a complementary set of strengths
and weaknesses best suited to regression problems and
homogeneous associations. Both approaches yield largely
interpretable solutions. An ideal ML algorithm would
have the capacity to adapt and blend representation to
best suit the problem at hand. In order to combine the
strengths of these respective algorithm
representations, a framework allowing coexistence and
co-evolution of trees and rules is needed. In this
work, we lay the empirical groundwork for such a
framework by demonstrating the capability of GP trees
to be evolved within an LCS-algorithm framework with
comparable performance to a set of standard GP
frameworks. We discuss how these results support the
feasibility of a GP-LCS framework and next-step
challenges to be addressed.",
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notes = "Also known as \cite{10.1145/3377929.3390071}
GECCO-2020 A Recombination of the 29th International
Conference on Genetic Algorithms (ICGA) and the 25th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Siddharth Verma
Piyush Borole
Ryan J Urbanowicz
Citations