The Self-Optimization (SO) model can be considered as the third operational mode of the classical Hopfield Network, leveraging the power of associative memory to enhance optimization performance. Moreover, it has been argued to express characteristics of minimal agency, which renders it useful for the study of artificial life. In this article, we draw attention to another facet of the SO model: its capacity for creativity. Drawing on creativity studies, we argue that the model satisfies the necessary and sufficient conditions of a creative process. Moreover, we show that learning is needed to find creative outcomes above chance probability. Furthermore, we demonstrate that modifying the learning parameters in the SO model gives rise to four different regimes that can account for both creative products and inconclusive outcomes, thus providing a framework for studying and understanding the emergence of creative behaviors in artificial systems that learn.

翻译：自优化模型可视为经典Hopfield网络的第三种运行模式，它利用联想记忆的能力来提升优化性能。此外，该模型被认为表现出最小能动性特征，使其在人工生命研究中具有应用价值。本文聚焦于自优化模型的另一维度：其创造性潜能。基于创造力研究理论，我们论证该模型满足创造性过程的必要与充分条件。进一步研究表明，学习过程是获得超越随机概率的创造性成果的必要条件。此外，我们通过调整自优化模型的学习参数，揭示了四种不同的运行机制，这些机制既能解释创造性产物的生成，也能说明无果而终的情况，从而为研究和理解学习型人工系统中创造性行为的涌现提供了理论框架。