@inbook {39,
	title = {Tutorials at PPSN 2016},
	booktitle = {Parallel Problem Solving from Nature {\textendash} PPSN XIV: 14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings},
	year = {2016},
	pages = {1012{\textendash}1022},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {PPSN 2016 hosts a total number of 16 tutorials covering a broad range of current research in evolutionary computation. The tutorials range from introductory to advanced and specialized but can all be attended without prior requirements. All PPSN attendees are cordially invited to take this opportunity to learn about ongoing research activities in our field!},
	isbn = {978-3-319-45823-6},
	doi = {10.1007/978-3-319-45823-6_95},
	url = {http://dx.doi.org/10.1007/978-3-319-45823-6_95},
	author = {Doerr, Carola and Bredeche, Nicolas and Alba, Enrique and Bartz-Beielstein, Thomas and Brockhoff, Dimo and Doerr, Benjamin and Eiben, Gusz and Epitropakis, Michael G. and Fonseca, Carlos M. and Guerreiro, Andreia and Haasdijk, Evert and Heinerman, Jacqueline and Hubert, Julien and Lehre, Per Kristian and Malag{\`o}, Luigi and Merelo, J. J. and Miller, Julian and Naujoks, Boris and Oliveto, Pietro and Picek, Stjepan and Pillay, Nelishia and Preuss, Mike and Ryser-Welch, Patricia and Squillero, Giovanni and Stork, J{\"o}rg and Sudholt, Dirk and Tonda, Alberto and Whitley, Darrell and Zaefferer, Martin},
	editor = {Handl, Julia and Hart, Emma and Lewis, Peter R. and L{\'o}pez-Ib{\'a}{\~n}ez, Manuel and Ochoa, Gabriela and Paechter, Ben}
}
@inbook {10,
	title = {Tracking Differential Evolution Algorithms: An Adaptive Approach through Multinomial Distribution Tracking with Exponential Forgetting},
	booktitle = {Artificial Intelligence: Theories and Applications},
	series = {Lecture Notes in Computer Science},
	volume = {7297},
	year = {2012},
	pages = {214-222},
	publisher = {Springer Berlin / Heidelberg},
	organization = {Springer Berlin / Heidelberg},
	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 {\textquotedblleft}online{\textquotedblright} 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.},
	isbn = {978-3-642-30447-7},
	doi = {10.1007/978-3-642-30448-4_27},
	author = {M. G. Epitropakis and D. K. Tasoulis and N. G. Pavlidis and V. P. Plagianakos and M. N. Vrahatis},
	editor = {Ilias Maglogiannis and Vassilis P. Plagianakos and Ioannis Vlahavas}
}
@conference {8,
	title = {Tracking Particle Swarm Optimizers: An adaptive approach through multinomial distribution tracking with exponential forgetting},
	booktitle = {IEEE Congress on Evolutionary Computation, 2012. CEC 2012. (IEEE World Congress on Computational Intelligence)},
	year = {2012},
	month = {June},
	address = {Brisbane, Australia},
	abstract = {An active research direction in Particle Swarm Optimization (PSO) is the integration of PSO variants in adaptive, or self-adaptive schemes, in an attempt to aggregate their characteristics and their search dynamics. In this work we borrow ideas from adaptive filter theory to develop an {\textquotedblleft}online{\textquotedblright} algorithm adaptation framework. The proposed framework is based on tracking the parameters of a multinomial distribution to capture changes in the evolutionary process. As such, we design a multinomial distribution tracker to capture the successful evolution movements of three PSO variants. Extensive 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. On the majority of tested cases, the proposed framework achieves substantial performance gain, while it seems to identify accurately the most appropriate algorithm for the problem at hand.},
	doi = {10.1109/CEC.2012.6256425},
	author = {M. G. Epitropakis and D. K. Tasoulis and N. G. Pavlidis and V. P. Plagianakos and M. N. Vrahatis}
}
