Abstract
We describe an approach to artificially evolving a drawing robot using implicit fitness functions, which are designed to minimise any direct reference to the line patterns made by the robot. We employ this approach to reduce the constraints we place on the robot’s autonomy and increase its utility as a test bed for synthetically investigating creativity. We demonstrate the critical role of neural network architecture in the line patterns generated by the robot.
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© 2008 Springer-Verlag Berlin Heidelberg
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Bird, J., Husbands, P., Perris, M., Bigge, B., Brown, P. (2008). Implicit Fitness Functions for Evolving a Drawing Robot. In: Giacobini, M., et al. Applications of Evolutionary Computing. EvoWorkshops 2008. Lecture Notes in Computer Science, vol 4974. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78761-7_50
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DOI: https://doi.org/10.1007/978-3-540-78761-7_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78760-0
Online ISBN: 978-3-540-78761-7
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