abstract = "Generative art produces artistic output via
algorithmic design. Common examples include flow
fields, particle motion, and mathematical formula
visualization. Typically an art piece is generated with
the artist/programmer acting as a domain expert to
create the final output. A large amount of effort is
often spent manipulating and/or refining parameters or
algorithms and observing the resulting changes in
produced images. Small changes to parameters of the
various techniques can substantially alter the final
product. We present GenerativeGI, a proof of concept
evolutionary framework for creating generative art
based on an input suite of artistic techniques and
desired aesthetic preferences for outputs. GenerativeGI
encodes artistic techniques in a grammar, thereby
enabling multiple techniques to be combined and
optimized via a many-objective evolutionary algorithm.
Specific combinations of evolutionary objectives can
help refine outputs reflecting the aesthetic
preferences of the designer. Experimental results
indicate that GenerativeGI can successfully produce
more visually complex outputs than those found by
random search.",