SGP-DT: Semantic Genetic Programming Based on Dynamic Targets
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{Ruberto:2020:EuroGP,
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author = "Stefano Ruberto and Valerio Terragni and
Jason H. Moore",
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title = "{SGP-DT}: Semantic Genetic Programming Based on
Dynamic Targets",
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booktitle = "EuroGP 2020: Proceedings of the 23rd European
Conference on Genetic Programming",
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year = "2020",
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month = "15-17 " # apr,
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editor = "Ting Hu and Nuno Lourenco and Eric Medvet",
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series = "LNCS",
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volume = "12101",
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publisher = "Springer Verlag",
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address = "Seville, Spain",
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pages = "167--183",
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organisation = "EvoStar, Species",
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keywords = "genetic algorithms, genetic programming, Semantic GP,
Natural selection, Symbolic Regression, Residuals,
Linear scaling, Crossover, Mutation",
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isbn13 = "978-3-030-44093-0",
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URL = "https://valerio65.github.io/assets/pdf/ruberto-eurogp-2020.pdf",
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DOI = "doi:10.1007/978-3-030-44094-7_11",
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video_url = "https://www.youtube.com/watch?v=xOz8BVqsHGY",
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size = "16 pages",
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abstract = "Semantic GP is a promising approach that introduces
semantic awareness during genetic evolution. This paper
presents a new Semantic GP approach based on Dynamic
Target (SGP-DT) that divides the search problem into
multiple GP runs. The evolution in each run is guided
by a new (dynamic) target based on the residual errors.
To obtain the final solution, SGP-DT combines the
solutions of each run using linear scaling. SGP-DT
presents a new methodology to produce the offspring
that does not rely on the classic crossover. The
synergy between such a methodology and linear scaling
yields to final solutions with low approximation error
and computational cost. We evaluate SGP-DT on eight
well-known data sets and compare with e-lexicase, a
state-of-the-art evolutionary technique. SGP-DT
achieves small RMSE values, on average 23.19percent
smaller than the one of epsilon-lexicase.",
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notes = "Nominated for best paper.
Also known as \cite{ruberto-eurogp-2020} Slides:
https://valerio65.github.io/assets/pdf/ruberto-eurogp-2020-slides.pdf
http://www.evostar.org/2020/cfp_eurogp.php Part of
\cite{Hu:2020:GP} EuroGP'2020 held in conjunction with
EvoCOP2020, EvoMusArt2020 and EvoApplications2020",
- }
Genetic Programming entries for
Stefano Ruberto
Valerio Terragni
Jason H Moore
Citations