2026-06-01T07:13:57Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/326182026-02-03T12:09:48Zcom_10630_2254col_10630_37959

00925njm 22002777a 4500 dc Toutouh-el-Alamin, Jamal author Hemberg, Erik author O'Reilly, Una-May author 2019-07-13 Generative adversary networks (GANs) suffer from training pathologies such as instability and mode collapse. These pathologies mainly arise from a lack of diversity in their adversarial interactions. Evolutionary generative adversarial networks apply the principles of evolutionary computation to mitigate these problems. We hybridize two of these approaches that promote training diversity. One, E-GAN, at each batch, injects mutation diversity by training the (replicated) generator with three independent objective functions then selecting the resulting best performing generator for the next batch. The other, Lipizzaner, injects population diversity by training a two-dimensional grid of GANs with a distributed evolutionary algorithm that includes neighbor exchanges of additional training adversaries, performance based selection and population-based hyper-parameter tuning. We propose to combine mutation and population approaches to diversity improvement. We contribute a superior evolutionary GANs training method, Mustangs, that eliminates the single loss function used across Lipizzaner's grid. Instead, each training round, a loss function is selected with equal probability, from among the three E-GAN uses. Experimental analyses on standard benchmarks, MNIST and CelebA, demonstrate that Mustangs provides a statistically faster training method resulting in more accurate networks. Toutouh, J., Hemberg, E., & O'Reilly, U. M. (2019, July). Spatial evolutionary generative adversarial networks. In Proceedings of the genetic and evolutionary computation conference (pp. 472-480). DOI: 10.1145/2001858.2002076 https://hdl.handle.net/10630/32618 10.1145/2001858.2002076 Redes neuronales (Informática) Computación evolutiva Algoritmos computacionales Spatial evolutionary generative adversarial networks.