This chapter describes a novel way of complexifying artificial neural networks through topological reorganisation. The neural networks are reorganised to optimise their neural complexity, which is a measure of the information-theoretic complexity of the network. Complexification of neural networks here happens through rearranging connections, i.e. removing one or more connections and placing them elsewhere. The results verify that a structural reorganisation can help to increase the probability of discovering a neural network capable of adequately solving complex tasks. The networks and the methodology proposed are tested in a simulation of a mobile robot racing around a track.
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Jorgensen, T.D., Haynes, B., Norlund, C. (2009). Reorganising Artificial Neural Network Topologies. In: Ao, SI., Gelman, L. (eds) Advances in Electrical Engineering and Computational Science. Lecture Notes in Electrical Engineering, vol 39. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-2311-7_35
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DOI: https://doi.org/10.1007/978-90-481-2311-7_35
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