Abstract
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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Basa, T., Go, C.A., Yoo, KS., Lee, WH. (2006). Using Physiological Signals to Evolve Art. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2006. Lecture Notes in Computer Science, vol 3907. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11732242_60
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DOI: https://doi.org/10.1007/11732242_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33237-4
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