@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 {9,
	title = {Density Based Projection Pursuit Clustering},
	booktitle = {IEEE Congress on Evolutionary Computation, 2012. CEC 2012. (IEEE World Congress on Computational Intelligence)},
	year = {2012},
	month = {June},
	address = {Brisbane, Australia},
	abstract = {Clustering of high dimensional data is a very important task in Data Mining. In dealing with such data, we typically need to use methods like Principal Component Analysis and Projection Pursuit, to find interesting lower dimensional directions to project the data and hence reduce their dimensionality in a manageable size. In this work, we propose a new criterion of direction interestingness, which incorporates information from the density of the projected data. Subsequently, we utilize the Differential Evolution algorithm to perform optimization over the space of the projections and hence construct a new hierarchical clustering algorithmic scheme. The new algorithm shows promising performance over a series of real and simulated data.},
	doi = {10.1109/CEC.2012.6253006},
	author = {S. K. Tasoulis and M. G. Epitropakis and V. P. Plagianakos and D. K. Tasoulis}
}
@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}
}
@article {5,
	title = {Enhancing Differential Evolution Utilizing Proximity-based Mutation Operators},
	journal = {IEEE Transactions on Evolutionary Computation},
	volume = {15},
	year = {2011},
	pages = {99-119},
	abstract = {Differential evolution is a very popular optimization algorithm and considerable research has been devoted to the development of efficient search operators. Motivated by the different manner in which various search operators behave, we propose a novel framework based on the proximity characteristics among the individual solutions as they evolve. Our framework incorporates information of neighboring individuals, in an attempt to efficiently guide the evolution of the population toward the global optimum, without sacrificing the search capabilities of the algorithm. More specifically, the random selection of parents during mutation is modified, by assigning to each individual a probability of selection that is inversely proportional to its distance from the mutated individual. The proposed framework can be applied to any mutation strategy with minimal changes. In this paper, we incorporate this framework in the original differential evolution algorithm, as well as other recently proposed differential evolution variants. Through an extensive experimental study, we show that the proposed framework results in enhanced performance for the majority of the benchmark problems studied.},
	issn = {1089-778X},
	doi = {10.1109/TEVC.2010.2083670},
	author = {M. G. Epitropakis and D. K. Tasoulis and N. G. Pavlidis and V. P. Plagianakos and M. N. Vrahatis}
}
@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}
}
