TY - CHAP T1 - Tutorials at PPSN 2016 T2 - Parallel Problem Solving from Nature – PPSN XIV: 14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings Y1 - 2016 A1 - Doerr, Carola A1 - Bredeche, Nicolas A1 - Alba, Enrique A1 - Bartz-Beielstein, Thomas A1 - Brockhoff, Dimo A1 - Doerr, Benjamin A1 - Eiben, Gusz A1 - Epitropakis, Michael G. A1 - Fonseca, Carlos M. A1 - Guerreiro, Andreia A1 - Haasdijk, Evert A1 - Heinerman, Jacqueline A1 - Hubert, Julien A1 - Lehre, Per Kristian A1 - Malagò, Luigi A1 - Merelo, J. J. A1 - Miller, Julian A1 - Naujoks, Boris A1 - Oliveto, Pietro A1 - Picek, Stjepan A1 - Pillay, Nelishia A1 - Preuss, Mike A1 - Ryser-Welch, Patricia A1 - Squillero, Giovanni A1 - Stork, Jörg A1 - Sudholt, Dirk A1 - Tonda, Alberto A1 - Whitley, Darrell A1 - Zaefferer, Martin ED - Handl, Julia ED - Hart, Emma ED - Lewis, Peter R. ED - López-Ibáñez, Manuel ED - Ochoa, Gabriela ED - Paechter, Ben AB - 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! JF - Parallel Problem Solving from Nature – PPSN XIV: 14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings PB - Springer International Publishing CY - Cham SN - 978-3-319-45823-6 UR - http://dx.doi.org/10.1007/978-3-319-45823-6_95 ER - TY - CONF T1 - Musical Composer Identification through Probabilistic and Feedforward Neural Networks T2 - Applications of Evolutionary Computation Y1 - 2010 A1 - M. A. Kaliakatsos-Papakostas A1 - M. G. Epitropakis A1 - M. N. Vrahatis ED - Di Chio, Cecilia ED - Brabazon, Anthony ED - Di Caro, Gianni ED - Ebner, Marc ED - Farooq, Muddassar ED - Fink, Andreas ED - Grahl, Jörn ED - Greenfield, Gary ED - Machado, Penousal ED - O’Neill, Michael ED - Tarantino, Ernesto ED - Urquhart, Neil AB - 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’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. JF - Applications of Evolutionary Computation PB - Springer Berlin / Heidelberg ER -