Jan. 20, 2013, 12:40 p.m.
Generative art
http://en.wikipedia.org/wiki/Generative_art
Generative art refers to art that in whole or in part has been created with the use of an autonomous system. An autonomous system in this context is one that is non-human and can independently determine features of an artwork that would otherwise require decisions made directly by the artist. In casual use "generative art" is often used to refer to computer generated artwork that is algorithmically determined. But generative art can also be made using systems of chemistry, biology, mechanics and robotics, smart materials, manual randomization, mathematics, data mapping, symmetry and tiling, and more. Wolfgang Amadeus Mozart's "Musikalisches Wrfelspiel" (Musical Dice Game) 1757 is an early example of a generative system based on randomness. Dice were used to select musical sequences from a numbered pool of previously composed phrases. This system provided a balance of order and disorder. The structure was based on an element of order on one hand, and disorder on the other. Composers such as John Cage[1] and Brian Eno[2] have used generative systems in their works. The artist Ellsworth Kelly created paintings by using chance operations to assign colors in a grid. He also created works on paper that he then cut into strips or squares and reassembled using chance operations to determine placement.[3] Artists such as Hans Haacke have explored processes of physical and social systems in artistic context. Franois Morellet has used both highly ordered and highly disordered systems in his artwork. Some of his paintings feature regular systems of radial or parallel lines to create Moir Patterns. In other works he has used chance operations to determine the coloration of grids. [4][5] Sol LeWitt created generative art in the form of systems expressed in natural language and systems of geometric permutation. Harold Cohen's AARON system is a longstanding project combining software artificial intelligence with robotic painting devices to create physical artifacts.,[6] Steina and Woody Vasulka are video art pioneers who used analog video feedback to create generative art. Video feedback is now cited as an example of deterministic chaos, and the early explorations by the Vasulkas anticipated contemporary science by many years. Software systems exploiting evolutionary computing to create visual form include those created by Scott Draves and Karl Sims. The digital artist Joseph Nechvatal has exploited models of viral contagion.[7] Autopoiesis by Ken Rinaldo includes fifteen musical and robotic sculptures that interact with the public and modify their behaviors based on both the presence of the participants and each other.[8] Maurizio Bolognini works with generative machines to address conceptual and social concerns.[9]. Mark Napier is a pioneer in data mapping, creating works based on the streams of zeros and ones in ethernet traffic, as part of the "Carnivore" project. Martin Wattenberg pushed this theme further, transforming "data sets" as diverse as musical scores (in "Shape of Song", 2001) and Wikipedia edits (History Flow, 2003, with Fernanda Viegas) into dramatic visual compositions. For some artists graphic user interfaces and computer code have become an independent art form in themselves. Adrian Ward created Auto-Illustrator as a commentary on software and generative methods applied to art and design. In 1987 Celestino Soddu created the artificial DNA of Italian Medieval towns able to generate endless 3D models of cities identifiable as belonging to the idea.[10] Writers such as Tristan Tzara, Brion Gysin, and William Burroughs used the cut-up technique to introduce randomization to literature as a generative system. 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. In the most widely cited theory of generative art Philip Galanter[11] was the first to describe generative art systems in the context of complexity theory. In particular the notion of Murray Gell-Mann and Seth Lloyd's effective complexity was cited. In this view both highly ordered and highly disordered generative art can be viewed as simple. Highly ordered generative art minimizes entropy and allows maximal data compression, and highly disordered generative art maximizes entropy and disallows significant data compression. Maximally complex generative art blends order and disorder in a manner similar to biological life, and indeed biologically inspired methods are most frequently used to create complex generative art. This view is at odds with the earlier information theory influenced views of Max Bense[12] and Abraham Moles[13] where complexity in art increases with disorder. There are two additional points worth noting. Galanter notes that given the use of visual symmetry, pattern, and repetition by the most ancient known cultures generative art is as old as art itself. He also addresses the mistaken equivalence by some that rule-based art is synonymous with generative art.[14] For example, some art is based on constraint rules that disallow the use of certain colors or shapes. Such art is not generative because constraint rules are not constructive, i. e. by themselves they don't assert what is to be done, only what cannot be done. In a later article Margaret Boden and Ernest Edmonds[15] present an overview of generative art and allied practices to develop a more precise language for critical discussion. They agree that generative art need not be restricted to that done using computers, and that some rule-based art is not generative. They go on to develop a technical vocabulary that includes Ele-art (electronic art), C-art (computer art), D-art (digital art), CA-art (computer assisted art), G-art (generative art), CG-art (computer based generative art), Evo-art (evolutionary based art), R-art (robotic art), I-art (interactive art), CI-art (computer based interactive art), and VR-art (virtual reality art). In both accounts the term generative art does not describe an art movement, ideology, or theory of aesthetics. The term refers to how the art is made, and does not take into account why it was made or what the content of the artwork is.
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