@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} } @article {30, title = {Chaos and Music: From time series analysis to evolutionary composition}, journal = {International Journal of Bifurcation and Chaos (IJBC)}, volume = {23}, year = {2013}, pages = {1350181}, abstract = {Music is an amalgam of logic and emotion, order and dissonance, along with many combinations of contradicting notions which allude to deterministic chaos. Therefore, it comes as no surprise that several research works have examined the utilization of dynamical systems for symbolic music composition. The main motivation of the paper at hand is the analysis of the tonal composition potentialities of several discrete dynamical systems, in comparison to genuine human compositions. Therefore, a set of human musical compositions is utilized to provide {\textquoteleft}{\textquoteleft}compositional guidelines{\textquoteright}{\textquoteright} to several dynamical systems, the parameters of which are properly adjusted through evolutionary computation. This procedure exposes the extent to which a system is capable of composing tonal sequences that resemble human composition. In parallel, a time series analysis on the genuine compositions is performed, which firstly provides an overview of their dynamical characteristics and secondly, allows a comparative analysis with the dynamics of the artificial compositions. The results expose the tonal composition capabilities of the examined iterative maps, providing specific references to the tonal characteristics that they can capture.}, doi = {10.1142/S0218127413501812}, url = {http://www.worldscientific.com/doi/abs/10.1142/S0218127413501812}, author = {M. A. Kaliakatsos-Papakostas and M. G. Epitropakis and A. Floros and M. N. Vrahatis} } @article {4, title = {Controlling interactive evolution of 8-bit melodies with genetic programming}, journal = {Soft Computing - A Fusion of Foundations, Methodologies and Applications}, volume = {16}, year = {2012}, pages = {1997-2008}, abstract = {Automatic music composition and sound synthesis is a field of study that gains continuously increasing attention. The introduction of evolutionary computation has further boosted the research towards exploring ways to incorporate human supervision and guidance in the automatic evolution of melodies and sounds. This kind of human{\textendash}machine interaction belongs to a larger methodological context called interactive evolution (IE). For the automatic creation of art and especially for music synthesis, user fatigue requires that the evolutionary process produces interesting content that evolves fast. This paper addresses this issue by presenting an IE system that evolves melodies using genetic programming (GP). A modification of the GP operators is proposed that allows the user to have control on the randomness of the evolutionary process. The results obtained by subjective tests indicate that the utilization of the proposed genetic operators drives the evolution to more user-preferable sounds. }, issn = {1432-7643}, doi = {10.1007/s00500-012-0872-y}, author = {M. A. Kaliakatsos{\textendash}Papakostas and M. G. Epitropakis and A. Floros and M. N. Vrahatis} } @inbook {11, title = {Interactive Evolution of 8{\textendash}Bit Melodies with Genetic Programming towards Finding Aesthetic Measures for Sound}, booktitle = {Evolutionary and Biologically Inspired Music, Sound, Art and Design}, series = {Lecture Notes in Computer Science}, volume = {7247}, year = {2012}, pages = {141-152}, publisher = {Springer Berlin / Heidelberg}, organization = {Springer Berlin / Heidelberg}, abstract = {The efficient specification of aesthetic measures for music as a part of modelling human conception of sound is a challenging task and has motivated several research works. It is not only targeted to the creation of automatic music composers and raters, but also reinforces the research for a deeper understanding of human noesis. The aim of this work is twofold: first, it proposes an Interactive Evolution system that uses Genetic Programming to evolve simple 8{\textendash}bit melodies. The results obtained by subjective tests indicate that evolution is driven towards more user{\textendash}preferable sounds. In turn, by monitoring features of the melodies in different evolution stages, indications are provided that some sound features may subsume information about aesthetic criteria. The results are promising and signify that further study of aesthetic preference through Interactive Evolution may accelerate the progress towards defining aesthetic measures for sound and music.}, isbn = {978-3-642-29141-8}, doi = {10.1007/978-3-642-29142-5_13}, author = {M. A. Kaliakatsos{\textendash}Papakostas and M. G. Epitropakis and A. Floros and M. N. Vrahatis}, editor = {Penousal Machado and Juan Romero and Adrian Carballal} } @conference {13, title = {Weighted Markov Chain Model for Musical Composer Identification}, booktitle = {Applications of Evolutionary Computation}, year = {2011}, publisher = {Springer Berlin / Heidelberg}, organization = {Springer Berlin / Heidelberg}, abstract = {Several approaches based on the {\textquoteleft}Markov chain model{\textquoteright} have been proposed to tackle the composer identification task. In the paper at hand, we propose to capture phrasing structural information from inter onset and pitch intervals of pairs of consecutive notes in a musical piece, by incorporating this information into a weighted variation of a first order Markov chain model. Additionally, we propose an evolutionary procedure that automatically tunes the introduced weights and exploits the full potential of the proposed model for tackling the composer identification task between two composers. Initial experimental results on string quartets of Haydn, Mozart and Beethoven suggest that the proposed model performs well and can provide insights on the inter onset and pitch intervals on the considered musical collection.}, doi = {10.1007/978-3-642-20520-0_34}, 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 Drechsler, Rolf and Farooq, Muddassar and Grahl, J{\"o}rn and Greenfield, Gary and Prins, Christian and Romero, Juan and Squillero, Giovanni and Tarantino, Ernesto and Tettamanzi, Andrea and Urquhart, Neil and Uyar, A.} } @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} }