Evolution Strategies (ES) has been applied to optimization problems for a long period of time. A straightforward implementation of ES can be iteratively perturbing parameters in a pool and keeping those that are most fitting, which is simple yet inefficient. As a consequence, applying such a straightforward algorithm can lead to sub-optimal performance for art creativity. To overcome this generic issue in ES, recent advances have been proposed to improve the performance of ES algorithms. One such improvement is Policy Gradients with Parameter-Based Exploration (PGPE), which estimates gradients in a black-box fashion so the computation of fitness does not have to be differentiable per se.