Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/11047
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Communities of minima in local optima networks of combinatorial spaces
Authors: Daolio, Fabio
Tomassini, Marco
Verel, Sebastien
Ochoa, Gabriela
Contact Email: gabriela.ochoa@cs.stir.ac.uk
Keywords: Community structure
Optima networks
Combinatorial fitness landscapes
Issue Date: 1-May-2011
Publisher: Elsevier
Citation: Daolio F, Tomassini M, Verel S & Ochoa G (2011) Communities of minima in local optima networks of combinatorial spaces, Physica A: Statistical Mechanics and its Applications, 390 (9), pp. 1684-1694.
Abstract: In this work, we present a new methodology to study the structure of the configuration spaces of hard combinatorial problems. It consists in building the network that has as nodes the locally optimal configurations and as edges the weighted oriented transitions between their basins of attraction. We apply the approach to the detection of communities in the optima networks produced by two different classes of instances of a hard combinatorial optimization problem: the quadratic assignment problem (QAP). We provide evidence indicating that the two problem instance classes give rise to very different configuration spaces. For the so-called real-like class, the networks possess a clear modular structure, while the optima networks belonging to the class of random uniform instances are less well partitionable into clusters. This is convincingly supported by using several statistical tests. Finally, we briefly discuss the consequences of the findings for heuristically searching the corresponding problem spaces.
Type: Journal Article
URI: http://hdl.handle.net/1893/11047
DOI Link: http://dx.doi.org/10.1016/j.physa.2011.01.005
Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.
Affiliation: Computing Science - CSM Dept
University of Lausanne
University of Nice Sophia-Antipolis, France
Computing Science - CSM Dept

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