  Recently, more and more works have proposed to drive evolutionary algorithms
using machine learning models.Usually, the performance of such model based
evolutionary algorithms is highly dependent on the training qualities of the
adopted models.Since it usually requires a certain amount of data (i.e. the
candidate solutions generated by the algorithms) for model training, the
performance deteriorates rapidly with the increase of the problem scales, due
to the curse of dimensionality.To address this issue, we propose a
multi-objective evolutionary algorithm driven by the generative adversarial
networks (GANs).At each generation of the proposed algorithm, the parent
solutions are first classified into \emph{real} and \emph{fake} samples to
train the GANs; then the offspring solutions are sampled by the trained
GANs.Thanks to the powerful generative ability of the GANs, our proposed
algorithm is capable of generating promising offspring solutions in
high-dimensional decision space with limited training data.The proposed
algorithm is tested on 10 benchmark problems with up to 200 decision
variables.Experimental results on these test problems demonstrate the
effectiveness of the proposed algorithm.
