Genetic Algorithm for the Evolution of Feature Trajectories in Time-Dependent Arts

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David Birchfield: Genetic Algorithm for the Evolution of Feature Trajectories in Time-Dependent Arts. In: Generative Art 2003.



Especially in generative art, the creation of perceptible and compelling large-scale forms and hierarchical structures that unfold over time is a nontrivial challenge. Nonetheless, this is an important goal for artists, such as musicians and video artists, who work in time-dependent mediums. In my work as a composer, I often sketch curves and lines that plot the trajectories of how musical features will develop throughout a given piece of music. For example, I might imagine a piece that exhibits a timbral evolution moving from bright to dark and a density trajectory from dense to sparse. I have found that the design and implementation of multiple, independent musical feature trajectories allows for complex musical structures to emerge, and I have worked to design this capability into generative systems for the composition of music. In this paper I describe the motivations and goals associated with the design and implementation of a hierarchical, coevolutionary genetic algorithm comprised of a population of musical components. I concentrate on the aspects of the algorithm which enable the automated generation of feature trajectories through the use of artificial intelligence and an expanded definition of the genetic feature.

Extended Abstract


Used References

[1] Holland, J., Adaptation in Natural and Artificial Systems, University of Michigan, Ann Arbor, 1975.

[2] Husbands, P., Distributed Coevolutionary Genetic Algorithms for Multi-Criteria and Multi-Constraint Optimisation. Evolutionary Computing, AISB Workshop 1994: 150-165

[3] Stravinsky, I., Symphony of Psalms. Boosey and Hawkes, London, rev. 1948.


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