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)

Abstract

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.

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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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