Journal of Heuristics

, Volume 14, Issue 5, pp 519–547 | Cite as

Combining variable neighborhood search and estimation of distribution algorithms in the protein side chain placement problem

  • Roberto Santana
  • Pedro Larrañaga
  • José A. Lozano
Article

Abstract

The aim of this work is to introduce several proposals for combining two metaheuristics: variable neighborhood search (VNS) and estimation of distribution algorithms (EDAs). Although each of these metaheuristics has been previously hybridized in several ways, this paper constitutes the first attempt to combine both optimization methods.

The different ways of combining VNS and EDAs will be classified into three groups. In the first group, we will consider combinations where the philosophy underlying VNS is embedded in EDAs. Considering different neighborhood spaces (points, populations or probability distributions), we will obtain instantiations for the approaches in this group. The second group of algorithms is obtained when probabilistic models (or any other machine learning paradigm) are used in order to exploit the good and bad shakes of the randomly generated solutions in a reduced variable neighborhood search. The last group of algorithms contains the results of alternating VNS and EDAs.

An application of the first approach is presented in the protein side chain placement problem. The results obtained show the superiority of the hybrid algorithm in comparison with EDAs and VNS.

Keywords

VNS EDAs UMDA Protein folding Rotamers Protein side chain placement 

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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Roberto Santana
    • 1
  • Pedro Larrañaga
    • 1
  • José A. Lozano
    • 1
  1. 1.Intelligent Systems Group, Department of Computer Science and Artificial IntelligenceUniversity of the Basque CountrySan Sebastián–DonostiaSpain

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