  There are two classes of generative art approaches: neural, where a deep
model is trained to generate samples from a data distribution, and symbolic or
algorithmic, where an artist designs the primary parameters and an autonomous
system generates samples within these constraints. In this work, we propose a
new hybrid genre: neuro-symbolic generative art. As a preliminary study, we
train a generative deep neural network on samples from the symbolic approach.
We demonstrate through human studies that subjects find the final artifacts and
the creation process using our neuro-symbolic approach to be more creative than
the symbolic approach 61% and 82% of the time respectively.
