The Evolution of Art: Questions and Concepts

September 29, 2009
by David Gilmore (dbg09)

We’ve been kicking around some ideas about our project, how to technically accomplish it and how to make it as human competitive as possible. Here’s a quick rundown of the current thoughts and questions.

Neural Networks and Machine Learning:

  • Is it possible to create a loose neural network framework that can analyze an image pixel by pixel and make judgements about it with absolutely no training? If so, can these judgements be relevant and useful or are they destined to just be idiosyncratic and purposeless?
  • How important is it to teach the computer what bad art is? Could it also learn from static noise, poorly composed photographs? Is it important that we rate art before feeding it into the network so that it has more to work from than just “bad” or “good?”
  • Is the field of visual art too broad for the neural network to make any reasonable deductions? Should we focus on a specific style of art? If so, what would be the most effective choice?
  • Where the hell do we even start?
Generation and Evolution:
  • After reading about the “evolution” of the Mona Lisa (which is really more hill-climbing than GP), the idea of creating an image (and genetically representing it) as a collection of initially randomly placed (and added) polygons seems like an effective starting point. With a high enough polygon count the art could begin to approach realism and even a low polygon count would create a unique aesthetic style.
  • The fitness function will necessarily need to be linked to our neural network critics’ opinions. This needs to be on a scale – “bad” or “good” will not help us with the determination of the most fit genes in a population.
  • Mutation here may need to be at a higher rate than is common with most genetic programming projects; adding new polygons or changing their characteristics (color, opacity, position) seems crucial for population diversity.
  • Would it be possible or effective to identify specific parts of the image that are especially bad and select them for more heavy mutation or crossover in the reproduction phase?
  • When are we done? A maximum number of iterations or a minimum required fitness score?
Human Competitive Success:
  • If we can create something that we feel could potentially be placed in a gallery, how should it be presented? Does stating that it was generated entirely by a computer defeat the human competitive nature of the project? Along the same lines, could a series of images showing how a final piece of visual art evolved from nothing be seen as human competitive art?
  • Lee and I spoke briefly about the idea of evolving descriptions or backgrounds as a companion to the visual art we create. We both attended a lecture by Paul Bloom entitled “But is it art?: A case-study in the cognitive science of pleasure.” In his talk, Bloom emphasized the importance of author intent and meaning in the human appreciation of art, putting it above even aesthetic appeal in importance. A striking example he gave was the piece of art brings people to tears more than any other: The Rothko Chapel. The chapel contains a wall with several canvases painted entirely dark purple. There is no disturbing imagery or any realism at all. The reason these people were so deeply affected was because of their knowledge of the artist, that Rothko committed suicide not long after the completion of the paintings. Aesthetically the paintings are simple but the meaning conveyed by them is multi-dimensional. What if it was possible, using GP, to generate these detailed and emotionally powerful back stories and descriptions? Could that add another dimension to the art and make it more human competitive?
We’re still philosophizing at this point as it’s important to have all the basic concepts down before starting a technical application. Any input and feedback would be greatly appreciated.
-Ben Gilmore

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