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.

本研究探讨了进化生成算法在音乐制作中的应用，以保护和增强人类创造力。通过将人类反馈融入差分进化算法，我们创作了六首歌曲，这些歌曲被提交给国际唱片公司，都收到了合同的邀约。除了测试这些方法的商业可行性之外，本文还考察了使用传统机器学习方法与进化算法进行内容生成的长期影响。当前的生成技术继续扩大规模，计算机生成内容超过人类创造的潜力逐渐增大，这种趋势带来了风险，即耗尽人类创建的训练数据库，可能导致生成机器学习模型越来越依赖于其随机输入函数用于生成新内容。与无目标随机函数引导的内容生成未来相比，我们的方法允许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具有文化相关性。

用进化生成算法保护人类创造力：一个音乐生成的案例研究