Bayesian Approach to Discrete Optimization

  • Jonas Mockus
  • William Eddy
  • Audris Mockus
  • Linas Mockus
  • Gintaras Reklaitis
Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 17)

Abstract

Various heuristics are widely used in discrete optimization. The average results of heuristic optimization can be improved by the randomization and optimization of heuristic parameters using the BHA. Therefore we consider the discrete optimization as the main area of BHA application. The representation of discrete optimization as a multi-stage decision problem is also a convenient way to show how BHA works.

Keywords

Discrete Optimization Greedy Heuristic Prob Ability Restrict Candidate List Polynomial Randomization 
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 Science+Business Media Dordrecht 1997

Authors and Affiliations

  • Jonas Mockus
    • 1
    • 2
    • 3
  • William Eddy
    • 4
  • Audris Mockus
    • 5
  • Linas Mockus
    • 6
  • Gintaras Reklaitis
    • 6
  1. 1.Institute of Mathematics and InformaticsKaunas Technological UniversityVilniusLithuania
  2. 2.Vytautas Magnus UniversityVilniusLithuania
  3. 3.Vilnius Technical UniversityVilniusLithuania
  4. 4.Department of StatisticsCarnegie-Mellon UniversityPittsburghUSA
  5. 5.Lucent Technologies AT&T Bell LaboratoriesPittsburghUSA
  6. 6.School of Chemical EngineeringPurdue UniversityW. LafayetteUSA

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