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Using Surrogate Constraints in Genetic Algorithms for Solving Multidimensional Knapsack Problems

  • Christian Haul
  • Stefan Voß
Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 9)

Abstract

In the multidimensional knapsack problem (or multiconstraint zero-one knapsack problem) one has to decide on how to make efficient use of an entity which consumes multiple resources. The problem is known to be NP-hard, thus heuristics come into consideration for a solution. In this paper we investigate genetic algorithms as a solution approach. Surrogate constraints are generated by several different methods and are utilized as one of the stages in genetic algorithms for solving the multidimensional knapsack problem. This approach as a standalone method does not improve results but in conjunction with a greedy local search strategy results may be improved for problem instances with small object-constraint ratio.

Keywords

Genetic Algorithm Local Search Problem Instance Knapsack Problem Random Search 
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 New York 1998

Authors and Affiliations

  • Christian Haul
    • 1
  • Stefan Voß
    • 2
  1. 1.Fachbereich InformatikTechnische Universität DarmstadtDarmstadtGermany
  2. 2.Abt. ABWL, Wirtschaftsinformatik und InformationsmanagementTechnische Universität BraunschweigBraunschweigGermany

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