On Stopping Criteria for Genetic Algorithms

  • Martín Safe
  • Jessica Carballido
  • Ignacio Ponzoni
  • Nélida Brignole
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3171)

Abstract

In this work we present a critical analysis of various aspects associated with the specification of termination conditions for simple genetic algorithms. The study, which is based on the use of Markov chains, identifies the main difficulties that arise when one wishes to set meaningful upper bounds for the number of iterations required to guarantee the convergence of such algorithms with a given confidence level. The latest trends in the design of stopping rules for evolutionary algorithms in general are also put forward and some proposals to overcome existing limitations in this respect are suggested.

Keywords

stopping rule genetic algorithm Markov chains convergence analysis 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Martín Safe
    • 1
  • Jessica Carballido
    • 1
    • 2
  • Ignacio Ponzoni
    • 1
    • 2
  • Nélida Brignole
    • 1
    • 2
  1. 1.Grupo de Investigación y Desarrollo en Computación Científica (GIDeCC), Departamento de Ciencias e Ingeniería de la ComputaciónUniversidad Nacional del SurBahía BlancaArgentina
  2. 2.Planta Piloto de Ingeniería Química – CONICETComplejo CRIBABBBahía BlancaArgentina

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