@inbook {25,
	title = {Feature Extraction Using Pitch Class Profile Information Entropy},
	booktitle = {Mathematics and Computation in Music},
	series = {Lecture Notes in Computer Science},
	volume = {6726},
	year = {2011},
	pages = {354-357},
	publisher = {Springer Berlin / Heidelberg},
	organization = {Springer Berlin / Heidelberg},
	abstract = {Computer aided musical analysis has led a research stream to explore the description of an entire musical piece by a single value. Combinations of such values, often called global features, have been used for several identification tasks on pieces with symbolic music representation. In this work we extend some ideas that estimate information entropy of sections of musical pieces, to utilize the Pitch Class Profile information entropy for global feature extraction. Two approaches are proposed and tested, the first approach considers musical sections as overlapping sliding onset windows, while the second one as non-overlapping fixed-length time windows.},
	doi = {10.1007/978-3-642-21590-2_32},
	url = {http://dx.doi.org/10.1007/978-3-642-21590-2_32},
	author = {M. A. Kaliakatsos-Papakostas and M. G. Epitropakis and M. N. Vrahatis},
	editor = {Agon, Carlos and Andreatta, Moreno and Assayag, G{\'e}rard and Amiot, Emmanuel and Bresson, Jean and Mandereau, John}
}
@conference {12,
	title = {Finding Multiple Global Optima Exploiting Differential Evolution{\textquoteright}s Niching Capability},
	booktitle = {IEEE Symposium on Differential Evolution, 2011. SDE 2011. (IEEE Symposium Series on Computational Intelligence)},
	year = {2011},
	month = {April},
	address = {Paris, France},
	abstract = {Handling multimodal functions is a very important and challenging task in evolutionary computation community, since most of the real-world applications exhibit highly multi-modal landscapes. Motivated by the dynamics and the proximity characteristics of Differential Evolution{\textquoteright}s mutation strategies tending to distribute the individuals of the population to the vicinity of the problem{\textquoteright}s minima, we introduce two new Differential Evolution mutation strategies. The new mutation strategies incorporate spatial information about the neighborhood of each potential solution and exhibit a niching formation, without incorporating any additional parameter. Experimental results on eight well known multimodal functions and comparisons with some state-of-the-art algorithms indicate that the proposed mutation strategies are competitive and very promising, since they are able to reliably locate and maintain many global optima throughout the evolution process.},
	doi = {10.1109/SDE.2011.5952058},
	author = {M. G. Epitropakis and V. P. Plagianakos and M. N. Vrahatis}
}
