A semantic genetic programming framework based on dynamic targets
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gp-bibliography.bib Revision:1.8081
- @Article{Ruberto:GPEM,
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author = "Stefano Ruberto and Valerio Terragni and
Jason H. Moore",
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title = "A semantic genetic programming framework based on
dynamic targets",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2021",
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volume = "22",
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number = "4",
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pages = "463--493",
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month = dec,
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note = "Special Issue: Highlights of Genetic Programming 2020
Events",
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keywords = "genetic algorithms, genetic programming, Semantic GP,
Genetic Programming, Natural Selection, Symbolic
Regression, Residuals, Linear Scaling, Crossover,
Mutation",
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ISSN = "1389-2576",
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URL = "https://rdcu.be/cy6pf",
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DOI = "doi:10.1007/s10710-021-09419-3",
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size = "31 pages",
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abstract = "Semantic GP is a promising branch of GP that
introduces semantic awareness during genetic evolution
to improve various aspects of GP. 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 of
previous runs. 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 final solutions with low
approximation error and computational cost. We evaluate
SGP-DT on eleven well-known data sets and compare with
e-lexicase, a state-of-the-art evolutionary technique,
and seven Machine Learning techniques. SGP-DT achieves
small RMSE values, on average 23.19percent smaller than
the one of epsilon-lexicase. Tuning SGP-DT
configuration greatly reduces the computational cost
while still obtaining competitive results.",
- }
Genetic Programming entries for
Stefano Ruberto
Valerio Terragni
Jason H Moore
Citations