  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 (IGA) circumvents the problem through human-in-the-loop, subjective
evaluation of aesthetics, but is limited 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 investigate the relationship between image measures,
such as complexity, and human aesthetic evaluation. We use dimension reduction
methods to visualise both genotype and phenotype space in order to support the
exploration of new territory in a generative system. Convolutional Neural
Networks trained on the artist's prior aesthetic evaluations are used to
suggest new possibilities similar or between known high quality
genotype-phenotype mappings. We integrate this classification and discovery
system into a software tool for evolving complex generative art and design.
