Conserving Human Creativity with Evolutionary Generative Algorithms: A Case Study in Music Generation

TL;DR

This paper demonstrates how evolutionary algorithms, combined with human feedback, can produce commercially successful music and offers a human-centric approach to AI-generated content, contrasting with traditional machine learning methods.

Contribution

It introduces a novel integration of human feedback into evolutionary algorithms for music creation, emphasizing long-term cultural relevance and creative exploration.

Findings

Six songs produced received record label contracts

Evolutionary algorithms can generate commercially viable music

Human feedback enhances creativity and cultural relevance

Abstract

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.