  Synthetic data has a key role to play in data sharing by statistical agencies
and other generators of statistical data products. Generative Adversarial
Networks (GANs), typically applied to image synthesis, are also a promising
method for tabular data synthesis. However, there are unique challenges in
tabular data compared to images, eg tabular data may contain both continuous
and discrete variables and conditional sampling, and, critically, the data
should possess high utility and low disclosure risk (the risk of re-identifying
a population unit or learning something new about them), providing an
opportunity for multi-objective (MO) optimization. Inspired by MO GANs for
images, this paper proposes a smart MO evolutionary conditional tabular GAN
(SMOE-CTGAN). This approach models conditional synthetic data by applying
conditional vectors in training, and uses concepts from MO optimisation to
balance disclosure risk against utility. Our results indicate that SMOE-CTGAN
is able to discover synthetic datasets with different risk and utility levels
for multiple national census datasets. We also find a sweet spot in the early
stage of training where a competitive utility and extremely low risk are
achieved, by using an Improvement Score. The full code can be downloaded from
https://github.com/HuskyNian/SMO\_EGAN\_pytorch.
