TY - CHAP T1 - Tracking Differential Evolution Algorithms: An Adaptive Approach through Multinomial Distribution Tracking with Exponential Forgetting T2 - Artificial Intelligence: Theories and Applications Y1 - 2012 A1 - M. G. Epitropakis A1 - D. K. Tasoulis A1 - N. G. Pavlidis A1 - V. P. Plagianakos A1 - M. N. Vrahatis ED - Ilias Maglogiannis ED - Vassilis P. Plagianakos ED - Ioannis Vlahavas AB - Several Differential Evolution variants with modified search dynamics have been recently proposed, to improve the performance of the method. This work borrows ideas from adaptive filter theory to develop an “online” algorithmic adaptation framework. The proposed framework is based on tracking the parameters of a multinomial distribution to reflect changes in the evolutionary process. As such, we design a multinomial distribution tracker to capture the successful evolution movements of three Differential Evolution algorithms, in an attempt to aggregate their characteristics and their search dynamics. Experimental results on ten benchmark functions and comparisons with five state-of-the-art algorithms indicate that the proposed framework is competitive and very promising. JF - Artificial Intelligence: Theories and Applications T3 - Lecture Notes in Computer Science PB - Springer Berlin / Heidelberg VL - 7297 SN - 978-3-642-30447-7 ER -