CoInGP: Convolutional Inpainting with Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Jakobovic:2021:GECCO,
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author = "Domagoj Jakobovic and Luca Manzoni and Luca Mariot and
Stjepan Picek and Mauro Castelli",
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title = "{CoInGP}: Convolutional Inpainting with Genetic
Programming",
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booktitle = "Proceedings of the 2021 Genetic and Evolutionary
Computation Conference",
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year = "2021",
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editor = "Francisco Chicano and Alberto Tonda and
Krzysztof Krawiec and Marde Helbig and
Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and
Luis Paquete and Gabriela Ochoa and Jaume Bacardit and
Christian Gagne and Sanaz Mostaghim and
Laetitia Jourdan and Oliver Schuetze and Petr Posik and
Carlos Segura and Renato Tinos and Carlos Cotta and
Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and
Risto Miikkulainen and Bing Xue and Aneta Neumann and
Richard Allmendinger and Fuyuki Ishikawa and
Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton",
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pages = "795--803",
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address = "internet",
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series = "GECCO '21",
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month = jul # " 10-14",
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, Convolution,
Supervised learning, Prediction, Images, Inpainting",
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isbn13 = "9781450383509",
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URL = "http://www.human-competitive.org/sites/default/files/mariot_0.txt",
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URL = "http://www.human-competitive.org/sites/default/files/jmmpc_coingp.pdf",
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DOI = "doi:10.1145/3449639.3459346",
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size = "9 pages",
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abstract = "We investigate the use of Genetic Programming (GP) as
a convolutional predictor for missing pixels in images.
The training phase is performed by sweeping a sliding
window over an image, where the pixels on the border
represent the inputs of a GP tree. The output of the
tree is taken as the predicted value for the central
pixel. We consider two topologies for the sliding
window, namely the Moore and the Von Neumann
neighbourhood. The best GP tree scoring the lowest
prediction error over the training set is then used to
predict the pixels in the test set. We experimentally
assess our approach through two experiments. In the
first one, we train a GP tree over a subset of 1000
complete images from the MNIST dataset. The results
show that GP can learn the distribution of the pixels
with respect to a simple baseline predictor, with no
significant differences observed between the two
neighborhoods. In the second experiment, we train a GP
convolutional predictor on two degraded images,
removing around 20 percent of their pixels. In this
case, we observe that the Moore neighborhood works
better, although the Von Neumann neighborhood allows
for a larger training set.",
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notes = "Entered 2021 HUMIES
University of Zagreb, Croatia
GECCO-2021 A Recombination of the 30th International
Conference on Genetic Algorithms (ICGA) and the 26th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Domagoj Jakobovic
Luca Manzoni
Luca Mariot
Stjepan Picek
Mauro Castelli
Citations