Journal of Heuristics

, Volume 16, Issue 6, pp 881–909 | Cite as

Autonomous operator management for evolutionary algorithms

  • Jorge Maturana
  • Frédéric Lardeux
  • Frédéric Saubion
Article

Abstract

The performance of an evolutionary algorithm strongly depends on the design of its operators and on the management of these operators along the search; that is, on the ability of the algorithm to balance exploration and exploitation of the search space. Recent approaches automate the tuning and control of the parameters that govern this balance. We propose a new technique to dynamically control the behavior of operators in an EA and to manage a large set of potential operators. The best operators are rewarded by applying them more often. Tests of this technique on instances of 3-SAT return results that are competitive with an algorithm tailored to the problem.

Keywords

Parameter control Adaptive search Hyper-heuristics Algorithm design 

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Jorge Maturana
    • 1
  • Frédéric Lardeux
    • 2
  • Frédéric Saubion
    • 2
  1. 1.Instituto de InformáticaUniversidad Austral de ChileValdiviaChile
  2. 2.Laboratoire LERIA, Département Informatique, UFR SciencesUniversité d’AngersAngersFrance

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