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.

翻译：本研究探讨了进化生成算法在音乐制作中的应用，旨在保护并增强人类创造力。通过将人类反馈融入差分进化算法，我们创作了六首歌曲并提交至国际唱片公司，所有作品均获得了签约意向。除了验证这些方法的商业可行性外，本文还比较了传统机器学习方法与进化算法在内容生成方面的长期影响。具体而言，随着当前生成技术的持续扩展，计算机生成内容超越人类创作的可能性日益增加。这一趋势可能导致人类创作训练数据的枯竭，迫使生成式机器学习模型越来越依赖其随机输入函数来产生新颖内容。相较于未来可能出现的由无目标随机函数主导的内容生成模式，我们的方法支持个性化的创造性探索，确保计算机辅助内容生成方法始终以人为中心并保持文化相关性。