  This study explores the application of evolutionary generative algorithms in
music production to preserve and enhance human creativity. By integrating human
feedback into Differential Evolution algorithms, we produced six songs that
were submitted to international record labels, all of which received contract
offers. In addition to testing the commercial viability of these methods, this
paper examines the long-term implications of content generation using
traditional machine learning methods compared with evolutionary algorithms.
Specifically, as current generative techniques continue to scale, the potential
for computer-generated content to outpace human creation becomes likely. This
trend poses a risk of exhausting the pool of human-created training data,
potentially forcing generative machine learning models to increasingly depend
on their random input functions for generating novel content. In contrast to a
future of content generation guided by aimless random functions, our approach
allows for individualized creative exploration, ensuring that computer-assisted
content generation methods are human-centric and culturally relevant through
time.
