Substructural Neighborhoods for Local Search in the Bayesian Optimization Algorithm

  • Claudio F. Lima
  • Martin Pelikan
  • Kumara Sastry
  • Martin Butz
  • David E. Goldberg
  • Fernando G. Lobo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4193)


This paper studies the utility of using substructural neighborhoods for local search in the Bayesian optimization algorithm (BOA). The probabilistic model of BOA, which automatically identifies important problem substructures, is used to define the structure of the neighborhoods used in local search. Additionally, a surrogate fitness model is considered to evaluate the improvement of the local search steps. The results show that performing substructural local search in BOA significatively reduces the number of generations necessary to converge to optimal solutions and thus provides substantial speedups.


Local Search Bayesian Network Memetic Algorithm Estimate Fitness Correct Linkage 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Claudio F. Lima
    • 1
  • Martin Pelikan
    • 2
  • Kumara Sastry
    • 3
  • Martin Butz
    • 4
  • David E. Goldberg
    • 3
  • Fernando G. Lobo
    • 1
  1. 1.University of AlgarvePortugal
  2. 2.University of Missouri at St. LouisUSA
  3. 3.University of Illinois at Urbana-ChampaignUSA
  4. 4.University of WürzburgGermany

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