Adaptive operator selection with dynamic multi-armed bandits

An important step toward self-tuning Evolutionary Algo-rithms is to design efficient Adaptive Operator Selection procedures. Such a procedure is made of two main com-ponents: a credit assignment mechanism, that computes a reward for each operator at hand based on some characteris-tics of the past offspring; and an adaptation rule, that mod-ifies 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 ba-sic 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 scenar-ios, after a careful sensitivity analysis of all methods. The Dynamic Multi-Armed Bandit method is found to outper-form the other methods on a scenario from the literature, while on another scenario, the basic Multi-Armed Bandit performs best