A Genetic-Based Solution to the Task-Based Sailor Assignment Problem

  • Dipankar Dasgupta
  • Deon Garrett
  • Fernando Nino
  • Alex Banceanu
  • David Becerra


This chapter presents a study investigating a multi-objective formulation of the United States Navy’s Task-based Sailor Assignment Problem and examines the performance of a widely used multi-objective evolutionary algorithm (MOEA), namely NSGA-II, on large instances of this problem. The performance of the evolutionary algorithm is examined with respect to both solution quality and diversity and has shown to provide inadequate diversity along the Pareto front. Domain-specific local improvement operators were introduced into the MOEA, producing significant performance increases over the evolutionary algorithm alone. Thus, hybrid MOEAs provided greater diversity along the Pareto front. Also a parallel version of the evolutionary algorithm was implemented. Particularly, an island model implementation was investigated. Exhaustive experimentations of the sequential and parallel implementations were carried out. The experimental results show that the genetic-based solution presented here is suitable for these types of problems.


Time Slot Pareto Front Assignment Problem Multiobjective Optimization Parallel Implementation 
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 2012

Authors and Affiliations

  • Dipankar Dasgupta
    • 1
  • Deon Garrett
    • 2
  • Fernando Nino
    • 3
  • Alex Banceanu
    • 1
  • David Becerra
    • 3
  1. 1.Department of Computer ScienceUniversity of MemphisMemphisUSA
  2. 2.Icelandic Institute for Intelligent Machines/School of Computer ScienceReykjavik UniversityReykjavikiceland
  3. 3.Department of Computer ScienceNational University of ColombiaBogotaColombia

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