  Generative adversarial networks (GANs) are machine learning models that are
used to estimate the underlying statistical structure of a given dataset and as
a result can be used for a variety of tasks such as image generation or anomaly
detection. Despite their initial simplicity, designing an effective loss
function for training GANs remains challenging, and various loss functions have
been proposed aiming to improve the performance and stability of the generative
models. In this study, loss function design for GANs is presented as an
optimization problem solved using the genetic programming (GP) approach.
Initial experiments were carried out using small Deep Convolutional GAN (DCGAN)
model and the MNIST dataset, in order to search experimentally for an improved
loss function. The functions found were evaluated on CIFAR10, with the best
function, named GANetic loss, showing exceptionally better performance and
stability compared to the losses commonly used for GAN training. To further
evalute its general applicability on more challenging problems, GANetic loss
was applied for two medical applications: image generation and anomaly
detection. Experiments were performed with histopathological, gastrointestinal
or glaucoma images to evaluate the GANetic loss in medical image generation,
resulting in improved image quality compared to the baseline models. The
GANetic Loss used for polyp and glaucoma images showed a strong improvement in
the detection of anomalies. In summary, the GANetic loss function was evaluated
on multiple datasets and applications where it consistently outperforms
alternative loss functions. Moreover, GANetic loss leads to stable training and
reproducible results, a known weak spot of GANs.
