Coevolutionary Computation for Adversarial Deep Learning
Name
3712255.3716533.pdf
Size
4.42 MB
Format
Adobe PDF
Checksum (MD5)
13018d2a30134b4de64a9e18294b0922
Author(s)
Toutouh, Jamal
O'Reilly, Una-May
Date Issued
August 11, 2025
Publisher
Association for Computing Machinery
Citation
Jamal Toutouh and Una-May O'Reilly. 2025. Coevolutionary Computation for Adversarial Deep Learning. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '25 Companion). Association for Computing Machinery, New York, NY, USA, 1663–1684.
Version
Final published version
Abstract
Learning Outcomes
-describe generative modeling and generative adversarial
networks (GANs)
-identify the intellectual intersection between GANs and
coevolutionary algorithms
-describe the design principles of a GAN and be familiar with
simple code for one
-use Python code to run a demonstration framework
Description
GECCO '25 Companion, July 14–18, 2025, Malaga, Spain
Terms of Use
Creative Commons Attribution-NonCommercial-ShareAlike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3712255.3716533
