  Evolutionary algorithms have been used in the digital art scene since the
1970s. A popular application of genetic algorithms is to optimize the
procedural placement of vector graphic primitives to resemble a given painting.
In recent years, deep learning-based approaches have also been proposed to
generate procedural drawings, which can be optimized using gradient descent. In
this work, we revisit the use of evolutionary algorithms for computational
creativity. We find that modern evolution strategies (ES) algorithms, when
tasked with the placement of shapes, offer large improvements in both quality
and efficiency compared to traditional genetic algorithms, and even comparable
to gradient-based methods. We demonstrate that ES is also well suited at
optimizing the placement of shapes to fit the CLIP model, and can produce
diverse, distinct geometric abstractions that are aligned with human
interpretation of language. Videos and demo: https://es-clip.github.io/
