Solving the exponential growth of symbolic regression trees in geometric semantic genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Martins:2018:GECCO,
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author = "Joao Francisco B. S. Martins and
Luiz Otavio V. B. Oliveira and Luis F. Miranda and Felipe Casadei and
Gisele L. Pappa",
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title = "Solving the exponential growth of symbolic regression
trees in geometric semantic genetic programming",
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booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary
Computation Conference",
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year = "2018",
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editor = "Hernan Aguirre and Keiki Takadama and
Hisashi Handa and Arnaud Liefooghe and Tomohiro Yoshikawa and
Andrew M. Sutton and Satoshi Ono and Francisco Chicano and
Shinichi Shirakawa and Zdenek Vasicek and
Roderich Gross and Andries Engelbrecht and Emma Hart and
Sebastian Risi and Ekart Aniko and Julian Togelius and
Sebastien Verel and Christian Blum and Will Browne and
Yusuke Nojima and Tea Tusar and Qingfu Zhang and
Nikolaus Hansen and Jose Antonio Lozano and
Dirk Thierens and Tian-Li Yu and Juergen Branke and
Yaochu Jin and Sara Silva and Hitoshi Iba and
Anna I Esparcia-Alcazar and Thomas Bartz-Beielstein and
Federica Sarro and Giuliano Antoniol and Anne Auger and
Per Kristian Lehre",
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isbn13 = "978-1-4503-5618-3",
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pages = "1151--1158",
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address = "Kyoto, Japan",
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DOI = "doi:10.1145/3205455.3205593",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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month = "15-19 " # jul,
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organisation = "SIGEVO",
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keywords = "genetic algorithms, genetic programming",
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abstract = "advances in Geometric Semantic Genetic Programming
(GSGP) have shown that this variant of Genetic
Programming (GP) reaches better results than its
predecessor for supervised machine learning problems,
particularly in the task of symbolic regression.
However, by construction, the geometric semantic
crossover operator generates individuals that grow
exponentially with the number of generations, resulting
in solutions with limited use. This paper presents a
new method for individual simplification named GSGP
with Reduced trees (GSGP-Red). GSGP-Red works by
expanding the functions generated by the geometric
semantic operators. The resulting expanded function is
guaranteed to be a linear combination that, in a second
step, has its repeated structures and respective
coefficients aggregated. Experiments in 12 real-world
datasets show that it is not only possible to create
smaller and completely equivalent individuals in
competitive computational time, but also to reduce the
number of nodes composing them by 58 orders of
magnitude, on average.",
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notes = "Also known as \cite{3205593} GECCO-2018 A
Recombination of the 27th International Conference on
Genetic Algorithms (ICGA-2018) and the 23rd Annual
Genetic Programming Conference (GP-2018)",
- }
Genetic Programming entries for
Joao Francisco B S Martins
Luiz Otavio Vilas Boas Oliveira
Luis Fernando Miranda
Felipe Casadei
Gisele L Pappa
Citations