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Partial structure learning by subset Walsh transform.

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



Yaochu Jin

Spencer Angus Thomas


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.


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:

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 IEEE Institute of Electrical and Electronics Engineers
Article Number 6651297
Pages 128-135
Series Title Proceedings of the UK workshop on computational intelligence
ISBN 9781479915682
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;
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