Exploring Evolutionary Generators within Generative Adversarial Networks
Created by W.Langdon from
gp-bibliography.bib Revision:1.7913
- @InProceedings{baeta:2024:GECCOcomp,
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author = "Francisco Baeta and Joao Correia and Tiago Martins and
Penousal Machado",
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title = "Exploring Evolutionary Generators within Generative
Adversarial Networks",
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booktitle = "Proceedings of the 2024 Genetic and Evolutionary
Computation Conference Companion",
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year = "2024",
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editor = "Jean-Baptiste Mouret and Kai Qin",
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pages = "251--254",
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address = "Melbourne, Australia",
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series = "GECCO '24",
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month = "14-18 " # jul,
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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, evolutionary
computation, generative adversarial networks, TGPGAN,
Evolutionary Machine Learning: Poster",
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isbn13 = "979-8-4007-0495-6",
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DOI = "doi:10.1145/3638530.3654348",
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size = "4 pages",
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abstract = "Since their introduction, Generative Adversarial
Networks (GANs) have represented the bulk of approaches
used in image generation. Before GANs, such approaches
used Machine Learning (ML) exclusively to tackle the
training problems inherent to GANs. However, in recent
years, evolutionary approaches have been making a
comeback, not only across the field of ML but in
generative modelling specifically. Successes in
GPU-accelerated Genetic Programming (GP) led to the
introduction of the TGPGAN framework, which used GP as
a replacement for the deep convolutional network
conventionally used as a GAN generator. In this paper,
we delve further into the generative capabilities of
evolutionary computation within adversarial models and
extend the study performed in TGPGAN to analyse other
evolutionary approaches. Similarly to TGPGAN, the
presented approaches replace the generator component of
a Deep Convolutional GAN (DCGAN): one with a
line-drawing Genetic Algorithm (GA) and another with a
Compositional Pattern Producing Network (CPPN). Our
comparison of generative performance shows that the GA
used manages to perform competitively with the original
framework. More importantly, this work showcases the
viability of other evolutionary approaches other than
GP for the purpose of image generation.",
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notes = "GECCO-2024 EML A Recombination of the 33rd
International Conference on Genetic Algorithms (ICGA)
and the 29th Annual Genetic Programming Conference
(GP)",
- }
Genetic Programming entries for
Francisco Baeta
Joao Nuno Goncalves Costa Cavaleiro Correia
Tiago Martins
Penousal Machado
Citations