Understanding nonlinear dynamics
Understanding nonlinear dynamics
On the dynamics of small continuous-time recurrent neural networks
Adaptive Behavior - Special issue on computational neuroethology
Musical networks
Frankensteinian methods for evolutionary music composition
Musical networks
Composing Music with Computers with Cdrom
Composing Music with Computers with Cdrom
Evolving 3d morphology and behavior by competition
Artificial Life
Acquiring Rules for Rules: Neuro-Dynamical Systems Account for Meta-Cognition
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
An Automated Music Improviser Using a Genetic Algorithm Driven Synthesis Engine
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
RaPScoM: towards composition strategies in a rapid score music prototyping framework
Proceedings of the 6th Audio Mostly Conference: A Conference on Interaction with Sound
Multi-feature visualisations of phenotypic behaviour for creative interactive evolution
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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This paper describes an ongoing exploration into the use of Continuous-Time Recurrent Neural Networks (CTRNNs) as generative and interactive performance tools, and using Genetic Algorithms (GAs) to evolve specific CTRNN behaviours. We propose that even randomly generated CTRNNs can be used in musically interesting ways, and that evolution can be employed to produce networks which exhibit properties that are suitable for use in interactive improvisation by computer musicians. We argue that the development of musical contexts for the CTRNN is best performed by the computer musician user rather than the programmer, and suggest ways in which strategies for the evolution of CTRNN behaviour may be developed further for this context.