PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Extreme Value Based Adaptive Operator Selection
Alvaro Fialho, Luis DaCosta, Marc Schoenauer and Michele Sebag
In: Parallel Problem Solving from Nature (PPSN'08), September 2008, Dortmund.

Abstract

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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:5119
Deposited By:Marc Schoenauer
Deposited On:24 March 2009