Tracking Differential Evolution Algorithms: An Adaptive Approach through Multinomial Distribution Tracking with Exponential Forgetting

Citation

Epitropakis M, Tasoulis DK, Pavlidis NG, Plagianakos VP & Vrahatis MN (2012) Tracking Differential Evolution Algorithms: An Adaptive Approach through Multinomial Distribution Tracking with Exponential Forgetting. In: Maglogiannis I, Plagianakos V & Vlahavas I (eds.) Artificial Intelligence: Theories and Applications: 7th Hellenic Conference on AI, SETN 2012, Lamia, Greece, May 28-31, 2012. Proceedings. Lecture Notes in Computer Science, 7297. 7th Hellenic Conference on AI, SETN 2012, Lamia, Greece, May 28-31, 2012, Lamia, Greece, 28.05.2012-31.05.2012. Berlin / Heidelberg: Springer, pp. 214-222. http://link.springer.com/chapter/10.1007/978-3-642-30448-4_27; https://doi.org/10.1007/978-3-642-30448-4_27

Abstract
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.

Keywords
Differential Evolution; Adaptation; Multinomial Distribution; Exponential forgetting