2008 AdaptiveOperatorSelectionwithDy

Subject Headings: Dynamic Multi-Armed Bandit.

Notes

An important step toward self-tuning Evolutionary Algorithms is to design efficient Adaptive Operator Selection procedures. Such a procedure is made of two main components: a credit assignment mechanism, that computes a reward for each operator at hand based on some characteristics of the past offspring; and an adaptation rule, that modifies the selection mechanism based on the rewards of the different operators. This paper is concerned with the latter, and proposes a new approach for it based on the well-known Multi-Armed Bandit paradigm. However, because the basic Multi-Armed Bandit methods have been developed for static frameworks, a specific Dynamic Multi-Armed Bandit algorithm is proposed, that hybridizes an optimal Multi-Armed Bandit algorithm with the statistical Page-Hinkley test, which enforces the efficient detection of changes in time series. This original Operator Selection procedure is then compared to the state-of-the-art rules known as [[Probability Matching and Adaptive Pursuit]] on several artificial scenarios, after a careful sensitivity analysis of all methods. The Dynamic Multi-Armed Bandit method is found to outperform the other methods on a scenario from the literature, while on another scenario, the basic Multi-Armed Bandit performs best.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 AdaptiveOperatorSelectionwithDy	Michèle Sebag Marc Schoenauer Luis DaCosta Alvaro Fialho			Adaptive Operator Selection with Dynamic Multi-armed Bandits				10.1145/1389095.1389272		2008