Parameter Search for Aesthetic Design and Composition

This thesis is about algorithmic creation in the arts – where an artist, designer or composer uses a formal generative process to assist in crafting forms and patterns – and approaches to finding effective input parameter values to these generative processes for aesthetic ends. Framed in three practical studies, approaches to navigating the aesthetic possibilities of generative processes in sound and visuals are presented, and strategies for eliciting the preferences of the consumers of the generated output are explored. The first study presents a musical interface that enables navigation of the possibilities of a stochastic generative process with respect to measures of subjective predictability. Through a mobile phone version of the application, aesthetic preferences are crowd-sourced. The second study presents an eye-tracking based framework for the exploration of the possibilities afforded by generative designs; the interaction between the viewers’ gaze patterns and the system engendering a fluid navigation of the state-space of the visual forms. The third study presents a crowd-sourced interactive evolutionary system, where populations of abstract colour images are shaped by thousands of preference selections from users worldwide For each study, the results of analyses eliciting the attributes of the generated outputs – and their associated parameter values – that are most preferred by the consumers/users of these systems are presented. Placed in a historical and theoretical context, a refined perspective on the complex interrelationships between generative processes, input parameters and perceived aesthetic value is presented. Contributions to knowledge include identified trends in objective aesthetic preferences in colour combinations and their arrangements, theoretical insights relating perceptual mechanisms to generative system design and analysis, strategies for effectively leveraging evolutionary computation in an empirical aesthetic context, and a novel eye-tracking based framework for the exploration of visual generative designs. Submitted for the degree of Doctor of Philosophy Queen Mary, University of London