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Terminating evolutionary algorithms at their steady state

Author

Listed:
  • Debora Gil
  • David Roche
  • Agnés Borràs
  • Jesús Giraldo

Abstract

Assessing the reliability of termination conditions for evolutionary algorithms (EAs) is of prime importance. An erroneous or weak stop criterion can negatively affect both the computational effort and the final result. We introduce a statistical framework for assessing whether a termination condition is able to stop an EA at its steady state, so that its results can not be improved anymore. We use a regression model in order to determine the requirements ensuring that a measure derived from EA evolving population is related to the distance to the optimum in decision variable space. Our framework is analyzed across 24 benchmark test functions and two standard termination criteria based on function fitness value in objective function space and EA population decision variable space distribution for the differential evolution (DE) paradigm. Results validate our framework as a powerful tool for determining the capability of a measure for terminating EA and the results also identify the decision variable space distribution as the best-suited for accurately terminating DE in real-world applications. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Debora Gil & David Roche & Agnés Borràs & Jesús Giraldo, 2015. "Terminating evolutionary algorithms at their steady state," Computational Optimization and Applications, Springer, vol. 61(2), pages 489-515, June.
    • Handle: RePEc:spr:coopap:v:61:y:2015:i:2:p:489-515
    DOI: 10.1007/s10589-014-9722-4
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    References listed on IDEAS

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    1. Mullen, Katharine M. & Ardia, David & Gil, David L. & Windover, Donald & Cline, James, 2011. "DEoptim: An R Package for Global Optimization by Differential Evolution," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i06).
    2. V. Ravikumar Pandi & B. K. Panigrahi, 2010. "Comparative Study of Evolutionary Computing Methods for Parameter Estimation of Power Quality Signals," International Journal of Applied Evolutionary Computation (IJAEC), IGI Global, vol. 1(2), pages 28-59, April.
    3. Aytug, Haldun & Koehler, Gary J., 2000. "New stopping criterion for genetic algorithms," European Journal of Operational Research, Elsevier, vol. 126(3), pages 662-674, November.
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