Extreme Value Based Adaptive Operator Selection

  • Álvaro Fialho
  • Luís Da Costa
  • Marc Schoenauer
  • Michèle Sebag
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)


Credit Assignment is an important ingredient of several proposals that have been made for Adaptive Operator Selection. Instead of the average fitness improvement of newborn offspring, this paper proposes to use some empirical order statistics of those improvements, arguing that rare but highly beneficial jumps matter as much or more than frequent but small improvements. An extreme value based Credit Assignment is thus proposed, rewarding each operator with the best fitness improvement observed in a sliding window for this operator. This mechanism, combined with existing Adaptive Operator Selection rules, is investigated in an EC-like setting. First results show that the proposed method allows both the Adaptive Pursuit and the Dynamic Multi-Armed Bandit selection rules to actually track the best operators along evolution.


Selection Rule Rogue Wave Probability Match Credit Assignment Multiarmed Bandit 
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

  • Álvaro Fialho
    • 1
  • Luís Da Costa
    • 2
  • Marc Schoenauer
    • 1
    • 2
  • Michèle Sebag
    • 1
    • 2
  1. 1.Microsoft Research-INRIA Joint CentreOrsay CedexFrance
  2. 2.Team TAO, INRIA Saclay - Île-de-France & LRI (UMR CNRS 8623), Bât. 490, Université Paris-SudOrsay CedexFrance

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