Network measures for re-using problem information in EDAs

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“Network measures for re-using problem information in EDAs” by R. Santana, C. Bielza, and P. Larrañaga, Department of Artificial Intelligence, Faculty of Informatics. Technical University of Madrid technical report UPM-FI/DIA/2010-3, June 2010.

Abstract

Probabilistic graphical models (PGMs) are used in estimation of distribution algorithms (EDAs) as a model of the search space. Graphical components of PGMs can be also analyzed as networks. In this paper we show that topological measures extracted from these networks capture characteristic information of the optimization problem. The measures can be also used to describe the EDA behavior. Using a simplified protein folding optimization problem, we show that the network information extracted from a set of problem instances can be effectively used to predict characteristics of similar instances.

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BibTeX entry:

@techreport{Santana_et_al:2010e,
   author = {R. Santana and C. Bielza and P. Larra{\~n}aga},
   title = {Network measures for re-using problem information in {EDAs}},
   institution = {Department of Artificial Intelligence, Faculty of
	Informatics, Technical University of Madrid},
   number = {UPM-FI/DIA/2010-3},
   month = jun,
   year = {2010}
}

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