Exploring Evolutionary and Biologically Inspired Music, Sound, Art and Design
EvoMUSART, the 9th International Conference (and 14th European event) on Evolutionary and Biologically Inspired Music, Sound, Art and Design was held online in April 2020.
SensiLab researchers presented two papers, the talks and links to the papers for each are available below.
This paper was presented at EvoMUSART 2020, which was meant to take place in Sevllle, Spain but due to Coronavirus had to be run completely online. The work is part of an ongoing collaboration between Prof. Jon McCormack at SensiLab and Andy Lomas at Goldsmiths in London.
Abstract: 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 geneotype 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.
You can download a preprint on arxiv.
AUTHORS. Jon McCormack, SensiLab, Monash University; Andy Lomas, Dept. of Computing, Goldsmiths, University of London.
WINNER: Best Paper EvoMUSART 2020.
ABSTRACT. Casual creators are creativity support tools designed for non-experts to have fun with while they create, rather than for serious creative production. We discuss here how we adapted and enhanced an evolutionary art approach for casual creation. Employing a fun-first methodology for the app design, we improved image production speed and the quality of randomly generated images. We further employed machine vision techniques for image categorisation and clustering and designed a user interface for fast, fun image generation, adhering to numerous principles arising from the study of casual creators. We describe the implementation and experimentation performed during the first stage of development, and evaluate the app in terms of efficiency, image quality, feedback quality and the potential for users to have fun. We conclude with a description of how the app will be used as a research tool, including a planned art installation.
AUTHORS: Simon Colton, Jon McCormack, Elena Petrovskaya, Sebastian Berns and Michael Cook.
The proceedings of EvoMUSART 2020 are available online following the link.
For more information about the conference and the full program, please visit the conference website.