UC San Diego
Evolutionary Sound: a Non-Symbolic Approach to Creating Sonic Art With Genetic Algorithms
- Author(s): Magnus, Cristyn
- Advisor(s): Puckette, Miller S
- et al.
The goal of this research is to explore the use of genetic algorithms to evolve waveforms. Genetic algorithms are introduced with a simple application that evolves novel tuning systems. Research involving the application of genetic algorithms to musical situations is reviewed. A framework for applying genetic algorithms is described in terms of virtual biology and virtual ecology. Virtual biology applies the central concepts of genetic algorithms (genetic representation, reproduction, fitness, mutation) to waveforms. Methods for implementing virtual biology are described in detail for time-domain waveforms and are proposed for spectral domain waveforms. Virtual ecologies replace the simple fitness function of conventional genetic algorithms. These ecologies can be designed to produce formal musical structure in an algorithm's output. Several algorithms that employ the framework are described in detail. The output of a simple version of the algorithm is systematically evaluated by running several fixed sets of sounds and environments with active evolutionary parameters. Works produced by more complex algorithms are evaluated subjectively. The success of this project is not in this algorithm's ability to make a population of sounds sound more like their environment, but rather in its ability to create novel sounds that are intimately tied to the process of their creation.