Gerard Howard
A spiking neural representation for XCSF
Howard, Gerard; Lanzi, Pier Luca; Howard, David; Bull, Larry
Authors
Pier Luca Lanzi
David Howard
Lawrence Bull Larry.Bull@uwe.ac.uk
School Director (Research & Enterprise) and Professor
Abstract
This paper presents a Learning Classifier System (LCS) where each traditional rule is represented by a spiking neural network, a type of network with dynamic internal state. The evolutionary design process exploits parameter self-adaptation and a constructionist approach, providing the system with a flexible knowledge representation. It is shown how this approach allows for the evolution of networks of appropriate complexity to emerge whilst solving a continuous maze environment. Additionally, we extend the system to allow for temporal state decomposition. We evaluate our spiking neural LCS against one that uses Multi Layer Perceptron rules. © 2010 IEEE.
Presentation Conference Type | Conference Paper (Published) |
---|---|
Conference Name | 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010 |
Start Date | Jul 18, 2010 |
End Date | Jul 23, 2010 |
Publication Date | Dec 1, 2010 |
Peer Reviewed | Not Peer Reviewed |
DOI | https://doi.org/10.1109/CEC.2010.5586035 |
Keywords | artificial neural networks, biological system modeling, brain models, neurons, robots, stability analysis |
Public URL | https://uwe-repository.worktribe.com/output/976887 |
Publisher URL | http://dx.doi.org/10.1109/CEC.2010.5586035 |
Additional Information | Title of Conference or Conference Proceedings : 2010 IEEE Congress on Evolutionary Computation (CEC) |
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