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Erschienen in:
2008 | OriginalPaper | Buchkapitel
Extreme Value Based Adaptive Operator Selection
verfasst von : Álvaro Fialho, Luís Da Costa, Marc Schoenauer, Michèle Sebag
Erschienen in: Parallel Problem Solving from Nature – PPSN X
Verlag: Springer Berlin Heidelberg
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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.