Partial structure learning by subset Walsh transform.

Christie, Lee A.; Lonie, David P.; McCall, John A.W.

doi:10.1109/UKCI.2013.6651297

Partial structure learning by subset Walsh transform.

Christie, Lee A.; Lonie, David P.; McCall, John A.W.

Authors

Dr Lee Christie l.a.christie@rgu.ac.uk
Research Fellow

Dr David Lonie d.p.lonie@rgu.ac.uk
Lecturer

Professor John McCall j.mccall@rgu.ac.uk
Director

Contributors

Yaochu Jin
Editor

Spencer Angus Thomas
Editor

Abstract

Estimation of distribution algorithms (EDAs) use structure learning to build a statistical model of good solutions discovered so far, in an effort to discover better solutions. The non-zero coefficients of the Walsh transform produce a hypergraph representation of structure of a binary fitness function; however, computation of all Walsh coefficients requires exhaustive evaluation of the search space. In this paper, we propose a stochastic method of determining Walsh coefficients for hyperedges contained within the selected subset of the variables (complete local structure). This method also detects parts of hyperedges which cut the boundary of the selected variable set (partial structure), which may be used to incrementally build an approximation of the problem hypergraph.

Citation

CHRISTIE, L.A., LONIE, D.P. and MCCALL, J.A.W. 2013. Partial structure learning by subset Walsh transform. In Jin, Y. and Thomas, S.A. (eds.) Proceedings of the 13th UK workshop on computational intelligence (UKCI 2013), 9-11 September 2013, Guildford, UK. New York: IEEE [online], article number 6651297, pages 128-135. Available from: https://doi.org/10.1109/UKCI.2013.6651297

Conference Name	13th UK workshop on computational intelligence (UKCI 2013)
Conference Location	Guildford, UK
Start Date	Sep 9, 2013
End Date	Sep 11, 2013
Acceptance Date	Sep 11, 2013
Online Publication Date	Sep 11, 2013
Publication Date	Oct 31, 2013
Deposit Date	Feb 19, 2016
Publicly Available Date	Feb 19, 2016
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Article Number	6651297
Pages	128-135
Series Title	Proceedings of the UK workshop on computational intelligence
ISBN	9781479915682
DOI	https://doi.org/10.1109/UKCI.2013.6651297
Keywords	Walsh functions; Distributed algorithms; Graph theory; Set theory; Stochastic processes; Transforms; Computational modeling; Educational institutions; Equations; Estimation; Standards; Symmetric matrices; EDA; Walsh coefficients; Binary fitness function;
Public URL	http://hdl.handle.net/10059/1387