The creative potential of Genetic Algorithms GAs has been explored by many musicians who attempt to harness the unbound possibilities for creative search evident in nature. Within this paper, we investigate the possibility of using Continuous Time Recurrent Neural Networks CTRNNs as an evolvable low-level audio synthesis structure, affording users access to a vast creative search space of audio possibilities. Specifically, we explore some initial GA designs through the development of Plecto see www.plecto.io, a creative tool that evolves CTRNNs for the discovery of audio. We have found that the evolution of CTRNNs offers some interesting prospects for audio exploration and present some design considerations for the implementation of such a system.
['Steffan Ianigro', 'Oliver Bown']
Plecto: A Low-Level Interactive Genetic Algorithm for the Evolution of Audio