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Learning the large-scale structure of the max-sat landscape using populations

Record type: Article

A new algorithm for solving MAX-SAT problems is introduced which clusters good solutions, and restarts the search from the closest feasible solution to the centroid of each cluster. This is shown to be highly efficient for finding good solutions of large MAX-SAT problems. We argue that this success is due to the population learning the large-scale structure of the fitness landscape. Systematic studies of the landscape are presented to support this hypothesis. In addition, a number of other strategies are tested to rule out other possible explanations of the success. Preliminary results are shown indicating that extensions of the proposed algorithm can give similar improvements on other hard optimisation problems.

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Citation

Qasem, Mohamed and Prugel-Bennett, Adam (2010) Learning the large-scale structure of the max-sat landscape using populations IEEE Transactions on Evolutionary Computation, 14, (4), pp. 518-529. (doi:10.1109/TEVC.2009.2033579).

More information

e-pub ahead of print date: 15 December 2009
Published date: August 2010
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 268060
URI: http://eprints.soton.ac.uk/id/eprint/268060
PURE UUID: de4c56b2-2fff-42d0-b4e7-17461f098e15

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Date deposited: 19 Oct 2009 08:34
Last modified: 18 Jul 2017 06:57

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Contributors

Author: Mohamed Qasem
Author: Adam Prugel-Bennett

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