Feature Engineering for Improving Robustness of Crossover in Symbolic Regression
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gp-bibliography.bib Revision:1.7954
- @InProceedings{Sambo:2020:GECCOcomp,
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author = "Aliyu Sani Sambo and R. Muhammad Atif Azad and
Yevgeniya Kovalchuk and
Vivek Padmanaabhan Indramohan and Hanifa Shah",
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title = "Feature Engineering for Improving Robustness of
Crossover in Symbolic Regression",
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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/10.1145/3377929.3390078",
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DOI = "doi:10.1145/3377929.3390078",
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booktitle = "Proceedings of the 2020 Genetic and Evolutionary
Computation Conference Companion",
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pages = "249--250",
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size = "2 pages",
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keywords = "genetic algorithms, genetic programming, feature
engineering, regression",
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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 = "Isolating the fitness-contribution of substructures is
typically a difficult task in Genetic Programming (GP).
Hence, useful substructures are lost when the overall
structure (model) performs poorly. Furthermore, while
crossover is heavily used in GP, it typically produces
offspring models with significantly lower fitness than
that of the parents. In symbolic regression, this
degradation also occurs because the coefficients of an
evolving model lose utility after crossover. This paper
proposes isolating the fitness-contribution of various
substructures and reducing the negative impact of
crossover by evolving a set of features instead of
monolithic models. The method then leverages multiple
linear regression (MLR) to optimise the coefficients of
these features. Since adding new features cannot
degrade the accuracy of an MLR produced model,
MLR-aided GP models can bloat. To penalise such
additions, we use Adjusted R2 as the fitness function.
The paper compares the proposed method with standard GP
and GP with linear scaling. Experimental results show
that the proposed method matches the accuracy of the
competing methods within only 1/10th of the number of
generations. Also, the method significantly decreases
the rate of post-crossover fitness degradation.",
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notes = "Also known as \cite{10.1145/3377929.3390078}
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
Aliyu Sani Sambo
R Muhammad Atif Azad
Yevgeniya Kovalchuk
Vivek Padmanaabhan Indramohan
Hanifa Shah
Citations