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Conference Papers Year : 2008

Extreme Value Based Adaptive Operator Selection

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Abstract

Credit Assignment is a crucial ingredient for successful 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.
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Dates and versions

inria-00287355 , version 1 (27-08-2008)
inria-00287355 , version 2 (03-12-2008)
inria-00287355 , version 3 (16-02-2009)

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Cite

Álvaro Fialho, Luis da Costa, Marc Schoenauer, Michèle Sebag. Extreme Value Based Adaptive Operator Selection. 10th International Conference on Parallel Problem Solving From Nature (PPSN X), Sep 2008, Dortmund, Germany. pp.175-184, ⟨10.1007/978-3-540-87700-4_18⟩. ⟨inria-00287355v3⟩
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