Artificial evolution for computer graphics
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Machine Learning
How to study artificial creativity
C&C '02 Proceedings of the 4th conference on Creativity & cognition
Mind-Mirror: EEG-Guided Image Evolution
Proceedings of the 13th International Conference on Human-Computer Interaction. Part II: Novel Interaction Methods and Techniques
Fitness in evolutionary art and music: what has been used and what could be used?
EvoMUSART'12 Proceedings of the First international conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design
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Human subjectivity have always posed a problem when it comes to judging designs. The line that divides what is interesting or not is blurred by the different interpretations as varied as the individuals themselves. Some approaches have made use of novelty in determining interestingness. However, computational measures of novelty such as the Euclidean distance are mere approximations to what the human brain finds interesting. In this paper, we explore the possibility of determining interestingness in a more direct method by using learning techniques such as Support Vector Machines to identify emotions from physiological signals, and then use genetic algorithms to evolve artworks that resulted in positive emotional signals.