@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}
}
@conference {17,
	title = {Musical Composer Identification through Probabilistic and Feedforward Neural Networks},
	booktitle = {Applications of Evolutionary Computation},
	year = {2010},
	publisher = {Springer Berlin / Heidelberg},
	organization = {Springer Berlin / Heidelberg},
	abstract = {During the last decade many efforts for music information retrieval have been made utilizing Computational Intelligence methods. Here, we examine the information capacity of the Dodecaphonic Trace Vector for composer classification and identification. To this end, we utilize Probabilistic Neural Networks for the construction of a similarity matrix of different composers and analyze the Dodecaphonic Trace Vector{\textquoteright}s ability to identify a composer through trained Feedforward Neural Networks. The training procedure is based on classical gradient-based methods as well as on the Differential Evolution algorithm. An experimental analysis on the pieces of seven classical composers is presented to gain insight about the most important strengths and weaknesses of the aforementioned approach.},
	doi = {10.1007/978-3-642-12242-2\_42},
	author = {M. A. Kaliakatsos-Papakostas and M. G. Epitropakis and M. N. Vrahatis},
	editor = {Di Chio, Cecilia and Brabazon, Anthony and Di Caro, Gianni and Ebner, Marc and Farooq, Muddassar and Fink, Andreas and Grahl, J{\"o}rn and Greenfield, Gary and Machado, Penousal and O{\textquoteright}Neill, Michael and Tarantino, Ernesto and Urquhart, Neil}
}
