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Autoencoding with a classifier system

Preen, Richard J.; Wilson, Stewart W.; Bull, Larry

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Dr Richard Preen
Senior Research Fellow in Machine Learning

Stewart W. Wilson

Lawrence Bull
School Director (Research & Enterprise) and Professor


Autoencoders are data-specific compression algorithms learned automatically from examples. The predominant approach has been to construct single large global models that cover the domain. However, training and evaluating models of increasing size comes at the price of additional time and computational cost. Conditional computation, sparsity, and model pruning techniques can reduce these costs while maintaining performance. Learning classifier systems (LCS) are a framework for adaptively subdividing input spaces into an ensemble of simpler local approximations that together cover the domain. LCS perform conditional computation through the use of a population of individual gating/guarding components, each associated with a local approximation. This article explores the use of an LCS to adaptively decompose the input domain into a collection of small autoencoders where local solutions of different complexity may emerge. In addition to benefits in convergence time and computational cost, it is shown possible to reduce code size as well as the resulting decoder computational cost when compared with the global model equivalent.

Journal Article Type Article
Acceptance Date May 1, 2021
Online Publication Date May 11, 2021
Publication Date 2021-12
Deposit Date May 7, 2021
Publicly Available Date May 12, 2021
Journal IEEE Transactions on Evolutionary Computation
Print ISSN 1089-778X
Electronic ISSN 1941-0026
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 25
Issue 6
Pages 1079 - 1090
Public URL


Autoencoding with a classifier system (1.4 Mb)


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