Implicit fitness functions for evolving a drawing robot

Authors:
Jon Bird;Phil Husbands;Martin Perris;Bill Bigge;Paul Brown
Affiliations:
Centre for Computational Neuroscience and Robotics, University of Sussex, Brighton, UK;Centre for Computational Neuroscience and Robotics, University of Sussex, Brighton, UK;Centre for Computational Neuroscience and Robotics, University of Sussex, Brighton, UK;Centre for Computational Neuroscience and Robotics, University of Sussex, Brighton, UK;Centre for Computational Neuroscience and Robotics, University of Sussex, Brighton, UK
Venue:
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Year:
2008

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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.