  Generative adversarial networks (GANs) exhibit training pathologies that can
lead to convergence-related degenerative behaviors, whereas
spatially-distributed, coevolutionary algorithms (CEAs) for GAN training, e.g.
Lipizzaner, are empirically robust to them. The robustness arises from
diversity that occurs by training populations of generators and discriminators
in each cell of a toroidal grid. Communication, where signals in the form of
parameters of the best GAN in a cell propagate in four directions: North,
South, West, and East, also plays a role, by communicating adaptations that are
both new and fit. We propose Lipi-Ring, a distributed CEA like Lipizzaner,
except that it uses a different spatial topology, i.e. a ring. Our central
question is whether the different directionality of signal propagation
(effectively migration to one or more neighbors on each side of a cell) meets
or exceeds the performance quality and training efficiency of Lipizzaner
Experimental analysis on different datasets (i.e, MNIST, CelebA, and COVID-19
chest X-ray images) shows that there are no significant differences between the
performances of the trained generative models by both methods. However,
Lipi-Ring significantly reduces the computational time (14.2%. . . 41.2%).
Thus, Lipi-Ring offers an alternative to Lipizzaner when the computational cost
of training matters.
