  A bottleneck in any evolutionary art system is aesthetic evaluation. Many
different methods have been proposed to automate the evaluation of aesthetics,
including measures of symmetry, coherence, complexity, contrast and grouping.
The interactive genetic algorithm (IGA) relies on human-in-the-loop, subjective
evaluation of aesthetics, but limits possibilities for large search due to user
fatigue and small population sizes. In this paper we look at how recent
advances in deep learning can assist in automating personal aesthetic
judgement. Using a leading artist's computer art dataset, we use dimensionality
reduction methods to visualise both genotype and phenotype space in order to
support the exploration of new territory in any generative system.
Convolutional Neural Networks trained on the user's prior aesthetic evaluations
are used to suggest new possibilities similar or between known high quality
genotype-phenotype mappings.
