  Generative Adversarial Networks (GAN) is an adversarial model, and it has
been demonstrated to be effective for various generative tasks. However, GAN
and its variants also suffer from many training problems, such as mode collapse
and gradient vanish. In this paper, we firstly propose a general crossover
operator, which can be widely applied to GANs using evolutionary strategies.
Then we design an evolutionary GAN framework C-GAN based on it. And we combine
the crossover operator with evolutionary generative adversarial networks (EGAN)
to implement the evolutionary generative adversarial networks with crossover
(CE-GAN). Under the premise that a variety of loss functions are used as
mutation operators to generate mutation individuals, we evaluate the generated
samples and allow the mutation individuals to learn experiences from the output
in a knowledge distillation manner, imitating the best output outcome,
resulting in better offspring. Then, we greedily selected the best offspring as
parents for subsequent training using discriminator as evaluator. Experiments
on real datasets demonstrate the effectiveness of CE-GAN and show that our
method is competitive in terms of generated images quality and time efficiency.
