  The training of generative adversarial networks (GANs) is usually vulnerable
to mode collapse and vanishing gradients. The evolutionary generative
adversarial network (E-GAN) attempts to alleviate these issues by optimizing
the learning strategy with multiple loss functions. It uses a learning-based
evolutionary framework, which develops new mutation operators specifically for
general deep neural networks. However, the evaluation mechanism in the fitness
function of E-GAN cannot truly reflect the adaptability of individuals to their
environment, leading to an inaccurate assessment of the diversity of
individuals. Moreover, the evolution step of E-GAN only contains mutation
operators without considering the crossover operator jointly, isolating the
superior characteristics among individuals. To address these issues, we propose
an improved E-GAN framework called IE-GAN, which introduces a new fitness
function and a generic crossover operator. In particular, the proposed fitness
function, from an objective perspective, can model the evolutionary process of
individuals more accurately. The crossover operator, which has been commonly
adopted in evolutionary algorithms, can enable offspring to imitate the
superior gene expression of their parents through knowledge distillation.
Experiments on various datasets demonstrate the effectiveness of our proposed
IE-GAN in terms of the quality of the generated samples and time efficiency.
