Generative Art Systems and Methods
Generative art systems can be categorized as being ordered, disordered, or complex. Here complex systems are those that have a mixture of both order and disorder and typically exhibit emergence.
Ordered generative art systems can include serial art, data mapping, the use of symmetry and tiling, number sequences and series, proportions such as the golden ratio, and combinatorics. Disordered generative art systems typically exploit some form of randomization, stochastics, or aspects of chaos theory.
While ordered generative art systems are as old as art itself, and disordered generative art systems came to prominence in the 20th century, contemporary generative art practice tends to lean in the direction of complex generative systems. Evolutionary computing approaches have been especially productive as a way to harness and steer complex expressions of aesthetic form and sound at a high level either by interactively choosing and breeding individual results leading to improved hybrids, or by applying automatic selection rules, or both.
Other computational generative systems that move towards complexity include diffusion-limited aggregation, L-systems, neural networks, cellular automata, reaction-diffusion systems, artificial life, and other biologically inspired methods such as swarm behaviour.
While some generative art exists as static artifacts produced by previous unseen processes, generative art can also be viewed developing in real-time. Typically such works are never displayed the same way twice. For example, graphical programming environments (e.g. Max/Msp, Pure Data or vvvv) as well as classic yet user-friendly programming environments such as Processing or openFrameworks are used to create real-time generative audiovisual artistic expressions in the Demoscene and in VJ-culture.
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