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. © 2008 Springer-Verlag Berlin Heidelberg.
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CITATION STYLE
Bird, J., Husbands, P., Perris, M., Bigge, B., & Brown, P. (2008). Implicit fitness functions for evolving a drawing robot. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4974 LNCS, pp. 473–478). https://doi.org/10.1007/978-3-540-78761-7_50
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