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Building anticipations in an accuracy-based learning classifier system by use of an artificial neural network

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2 Author(s)
O'Hara, T. ; Sch. of Comput. Sci., West of England Univ., Bristol, UK ; Bull, L.

Learning classifier systems which build anticipations of the expected states following their actions are a focus of current research. This paper presents a mechanism by which to create learning classifier systems of this type, here using accuracy-based fitness. In particular, we highlight the supervised learning nature of the anticipatory task and amend each rule of the system with a traditional artificial neural network. The system is described and shown able to perform well in a number of well-known maze tasks.

Published in:

Evolutionary Computation, 2005. The 2005 IEEE Congress on  (Volume:3 )

Date of Conference:

2-5 Sept. 2005

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