Controlling self-organization in generative creative systems

We present a new tool which simulates the development of Artificial Chemistries (AChems) to produce real-time imagery for artistic/entertainment purposes. There have been many such usages of complex systems (CSs) for artistic purposes, but deciding which parameters to use for such unpredictable systems can lead to a feeling of lack of control. For our purposes, we struggled to gain enough control over the AChem real-time image generation tool to accompany music in a video-jockeying application. To overcome this difficulty, we developed a general-purpose clustering approach that attempts to produce sets of parameter configurations which lead to maximally distinct visualisations, thus ensuring users feel that they have influence over the AChem when controlled with a suitable GUI. We present this approach and its application to controlling the development of AChems, along with the results from experiments with different clustering approaches, aided by both machine vision analysis and human curation. We conclude by advocating an overfitting approach supplemented by a final check by a designer, and discuss potential applications of this in artistic and entertainment settings.