This document presents the adaptive strategy selection Fitness-Based Area-Under-Curve Bandit (F-AUC-Bandit) and its use in the context of a continuous optimization algorithm: Differential Evolution. Experimental results were obtained on a testbed of noiseless functions. They demonstrate the interest of using an adaptive strategy selection technique as opposed to the naïve approach which consists in the use of a unique strategy of the optimization algorithm chosen initially. Performance comparisons are made between F-AUC-Bandit and a uniform strategy selection approach and also other adaptive selection strategies. Finally a comparison to the state-of-the-art CMA-ES optimizer is made. Results of the optimization algorithm using F-AUC-Bandit are still not comparable to those of CMA-ES but demonstrate a big improvement on the use of the basic DE.