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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Description/Abstract

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
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 > Comms, Signal Processing & Control
ePrint ID: 268060
Date Deposited: 19 Oct 2009 08:34
Last Modified: 27 Mar 2014 20:14
Further Information:Google Scholar
ISI Citation Count:5
URI: http://eprints.soton.ac.uk/id/eprint/268060

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