On the hybridization of geometric semantic GP with gradient-based optimizers
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @Article{Pietropolli:2023:GPEM,
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author = "Gloria Pietropolli and Luca Manzoni and
Alessia Paoletti and Mauro Castelli",
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title = "On the hybridization of geometric semantic {GP} with
gradient-based optimizers",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2023",
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volume = "24",
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number = "2",
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pages = "Article number: 16",
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month = dec,
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note = "Online first",
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keywords = "genetic algorithms, genetic programming, Geometric
semantic genetic programming, Stochastic gradient
descent, Adam, Evolutionary algorithm",
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ISSN = "1389-2576",
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URL = "https://rdcu.be/dpWsR",
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DOI = "doi:10.1007/s10710-023-09463-1",
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size = "20 pages",
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abstract = "Geometric semantic genetic programming (GSGP) is a
popular form of GP where the effect of crossover and
mutation can be expressed as geometric operations on a
semantic space. A recent study showed that GSGP can be
hybridized with a standard gradient-based optimized,
Adam, commonly used in training artificial neural
networks.We expand upon that work by considering more
gradient-based optimizers, a deeper investigation of
their parameters, how the hybridization is performed,
and a more comprehensive set of benchmark problems.
With the correct choice of hyperparameters, this
hybridization improves the performances of GSGP and
allows it to reach the same fitness values with fewer
fitness evaluations.",
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notes = "Dipartimento di Matematica e Geoscienze, Universita
degli Studi di Trieste, Via Alfonso Valerio 12/1,
Trieste, 34127, TS, Italy",
- }
Genetic Programming entries for
Gloria Pietropolli
Luca Manzoni
Alessia Paoletti
Mauro Castelli
Citations