Deep learning of individual aesthetics

@article{Mccormack2021DeepLO,
  title={Deep learning of individual aesthetics},
  author={Jon Mccormack and Andy Lomas},
  journal={Neural Computing and Applications},
  year={2021},
  volume={33},
  pages={3-17}
}
Accurate evaluation of human aesthetic preferences represents a major challenge for creative evolutionary and generative systems research. Prior work has tended to focus on feature measures of the artefact, such as symmetry, complexity and coherence. However, research models from psychology suggest that human aesthetic experiences encapsulate factors beyond the artefact, making accurate computational models very difficult to design. The interactive genetic algorithm circumvents the problem… 
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