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

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), 518-529. (doi:10.1109/TEVC.2009.2033579).


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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.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1109/TEVC.2009.2033579
ISSNs: 1089-778X (print)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions : Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Southampton Wireless Group
ePrint ID: 268060
Accepted Date and Publication Date:
August 2010Published
15 December 2009Made publicly available
Date Deposited: 19 Oct 2009 08:34
Last Modified: 31 Mar 2016 14:16
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/268060

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