A Steady-State Genetic Algorithm with Resampling for Noisy Inventory Control

  • Steven Prestwich
  • S. Armagan Tarim
  • Roberto Rossi
  • Brahim Hnich
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)


Noisy fitness functions occur in many practical applications of evolutionary computation. A standard technique for solving these problems is fitness resampling but this may be inefficient or need a large population, and combined with elitism it may overvalue chromosomes or reduce genetic diversity. We describe a simple new resampling technique called Greedy Average Sampling for steady-state genetic algorithms such as GENITOR. It requires an extra runtime parameter to be tuned, but does not need a large population or assumptions on noise distributions. In experiments on a well-known Inventory Control problem it performed a large number of samples on the best chromosomes yet only a small number on average, and was more effective than four other tested techniques.


Genetic Algorithm Reinforcement Learn Evolutionary Computation Inventory Control Noisy Environment 
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 2008

Authors and Affiliations

  • Steven Prestwich
    • 1
  • S. Armagan Tarim
    • 2
  • Roberto Rossi
    • 1
  • Brahim Hnich
    • 3
  1. 1.Cork Constraint Computation CentreUniversity CollegeCorkIreland
  2. 2.Department of ManagementHacettepe UniversityTurkey
  3. 3.Faculty of Computer ScienceIzmir University of EconomicsTurkey

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