GP-Based Generative Adversarial Models
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Machado:2022:GPTP,
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author = "Penousal Machado and Francisco Baeta and
Tiago Martins and Joao Correia",
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title = "{GP}-Based Generative Adversarial Models",
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booktitle = "Genetic Programming Theory and Practice XIX",
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year = "2022",
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editor = "Leonardo Trujillo and Stephan M. Winkler and
Sara Silva and Wolfgang Banzhaf",
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series = "Genetic and Evolutionary Computation",
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pages = "117--140",
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address = "Ann Arbor, USA",
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month = jun # " 2-4",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, ANN",
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isbn13 = "978-981-19-8459-4",
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DOI = "doi:10.1007/978-981-19-8460-0_6",
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abstract = "We explore the use of Artificial Neural Network
(ANN)-guided Genetic Programming (GP) to generate
images that the guiding network classifies as belonging
to a specific class. The experimental results
demonstrate the ability of GP to perform such a task
but also the inadequacy of most of the generated
images, which can be considered false positives. Based
on these findings and following an approach analogous
to Generative Adversarial Networks (GANs), we propose
an generative adversarial model where GP replaces the
traditional GAN's generator. The experimental results
illustrate the advantages of this approach,
highlighting the expressive power of GP, its capacity
to perform online learning, thus adapting to a dynamic
fitness landscape, and its ability to create novel
imagery that fits the target classes.",
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notes = "Part of \cite{Banzhaf:2022:GPTP} published after the
workshop in 2023",
- }
Genetic Programming entries for
Penousal Machado
Francisco Baeta
Tiago Martins
Joao Nuno Goncalves Costa Cavaleiro Correia
Citations